Abstract
We present here the analysis performed using the pyPipe3D pipeline for the final MaNGA data set included in the Sloan Digital Sky Survey data release 17. This data set comprises more than 10,000 individual data cubes, being the integral field spectroscopic (IFS) galaxy survey with the largest number of galaxies. pyPipe3D processes the IFS data cubes to extract spatially resolved spectroscopic properties of both the stellar population and the ionized gas emission lines. A brief summary of the properties of the sample and the characteristics of the analyzed data are included. The article provides details of: (i) the analysis performed; (ii) a description of the pipeline; (iii) the adopted stellar population library; (iv) the morphological and photometric analysis; (v) the adopted data model for the spatially resolved properties derived; and (vi) the individual integrated and characteristic galaxy properties included in the final catalog. Comparisons with the results from a previous version of the pipeline for earlier data releases and from other tools using this data set are included. A practical example of how to use the full data set and the final catalog illustrates how to handle the delivered product. Our full analysis can be accessed and downloaded from our web page.
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1. Introduction
Large imaging and spectroscopic galaxy surveys in the nearby universe, covering statistically well-defined samples—e.g., the Sloan Digital Sky Survey (SDSS; York et al. 2000) and the Galaxy and Mass Assembly survey (Driver et al. 2009)—have changed our understanding of galaxy evolution over the last few decades (e.g., Blanton et al. 2017). Their development and implementation have imposed substantial challenges, changing how we do science in astronomy. Among other things, they have led to the development of automatic or semiautomatic software packages that handle the reduction and analysis of these large data sets, i.e., pipelines. These pipelines have been extremely important for the development of the recent integral field spectroscopic galaxy surveys (IFS-GSs; for a recent review, see Sánchez 2020). The large number of spectra involved and their unique characteristics (the spatial continuity of the data) require the implementation of new analysis procedures and methods to store and distribute the results of these explorations. We remind the reader that any of the recent IFS-GS data releases (DRs) comprise millions of spectra (e.g., SAMI; Croom et al. 2012), and, at the same time, they may contain between hundreds (e.g., CALIFA, MaNGA; Sánchez et al. 2012; Bundy et al. 2015) and tens of thousands of individual spectra per galaxy (e.g., AMUSSING++; López-Cobá et al. 2020).
Different reduction and analysis pipelines have been developed as IFS-GSs have appeared over the last decade. In some cases, they adopted already available tools, like starlight (Cid Fernandes et al. 2005) or pPXF (Cappellari & Emsellem 2004), designed for the analysis of the spectra of stellar populations, in combination with ad hoc tools tailored to explore the emission-line component. This led to IFS analysis pipelines such as PyCASSO (de Amorim et al. 2017) and the MaNGA Data Analysis Pipeline (DAP; Belfiore et al. 2019; Westfall et al. 2019), developed to analyze specific data sets, CALIFA and MaNGA in this case. In other instances, new packages were developed from scratch, adapting existing algorithms to create multipurpose pipelines that were able to handle data from different surveys, e.g., Pipe3D (Sánchez et al. 2016a). The Pipe3D pipeline is based on the FIT3D package routines and algorithms (Sánchez et al. 2016b), which aim to extract the properties of both the stellar populations and the ionized gas producing the emission lines, from generic IFS data in the visible range. Pipe3D has been used to analyze data from different IFS-GSs, such as CALIFA (Sánchez-Menguiano et al. 2016; Espinosa-Ponce et al. 2020), SAMI (Sánchez et al. 2019b), and AMUSSING++ (Sánchez-Menguiano et al. 2018; López-Cobá et al. 2020). We analyzed the different MaNGA DRs with Pipe3D (e.g., Sánchez et al. 2018), resulting in sets of data products for each galaxy (cube), delivered to the community as parts of different Value-added Catalogs (VACs). 8
In this paper, we present the results of the analysis of the full MaNGA data set, distributed as part of SDSS DR17 (Abdurro'uf et al. 2022), using an updated version of the Pipe3D pipeline (pyPipe3D; Lacerda et al. 2022). The paper is structured as follows. Sections 2 and 3 contain a brief description of the sample of galaxies and the main characteristics of the data analyzed in this work, respectively. A detailed description of our analysis, focusing in particular on the differences with previous implementations of this pipeline, is included in Section 4. This section contains a brief description of the pipeline itself (Section 4.1), the adopted spectral library (Section 4.2), a newly introduced morphological classification (Section 4.3), the photometric and structural galaxy properties extracted from the data cubes (Section 4.4), and, finally, the quality control performed to validate our analysis (Section 4.5). The results of our analysis are then presented in Section 5, including a description of the Pipe3D data model adopted to distribute the results (Section 5.1) and a practical example of its use (Section 5.2). Section 5.3 contains the descriptions of the integrated and characteristic properties of each galaxy extracted from this analysis and included in the final delivered catalog. A comparison with the previous results obtained with this and other tools is included in Section 6. Section 7 includes an example of the use of the final catalog, updating the selection criteria for candidate galaxies to host an active galactic nucleus (AGN) presented in Sánchez et al. (2018). Section 8 summarizes our results. The full data products and the final catalog are freely available. 9
2. Sample
We analyze the full data set provided by the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (Bundy et al. 2015), i.e., more than 10,000 galaxies observed in the period between 2014 April and 2020 August. MaNGA is one of the three projects included in the fourth version of SDSS (SDSS-IV; Blanton et al. 2017). The sample of galaxies was selected from the NASA-Sloan Atlas (NSA), 10 a catalog of images and parameters of local galaxies (z ≲ 0.1), derived from the combination of UV (GALEX), optical (SDSS), and near-infrared (2MASS) images of galaxies, with spectroscopic information provided by the SDSS survey itself. From the NSA, MaNGA selected a representative sample of the population of galaxies in the nearby universe, aiming to: (i) obtain a flat distribution of stellar mass (adopting an absolute magnitude plus color proxy); (ii) sample the optical extension of galaxies up to a certain desired level; and (iii) obtain enough galaxies of any morphological type and in any environment to make possible statistically significant comparisons between different subsamples (Bundy et al. 2015). The final sample contains three main subsamples: (i) a primary sample, comprising ∼60% of the objects, selected so that the fields of view (FOVs) of the adopted integral field units (IFUs) cover at least a 1.5 galaxy effective radius (re ); (ii) a secondary sample, comprising ∼30% of the objects, such that the FOVs of the IFUs cover at least 2.5 re ; and (iii) a color-enhanced sample, comprising ∼10% of the galaxies, aiming to overpopulate the green valley (GV) between star-forming galaxies (SFGs) and retired galaxies (RGs), in order to obtain a statistically large enough number of galaxies in this regime to allow comparisons with the other two groups. Finally, a small number of galaxies were cherry-picked, to use unallocated IFUs, due to the limitations of the coverage and the availability of the galaxies in the three previous subsamples at a certain observing period. The latter galaxies correspond to a small percentage of the total sample. Details of the final sample selection are given in Wake et al. (2017). The complex sample selection and the final implementation of the observing program imply that the observed sample will not reproduce the expected distribution of galaxies in the nearby universe, unless a proper volume correction is applied (e.g., Sánchez et al. 2019a).
Figure 1 shows the distribution of the u − z color of the galaxies in the final sample, as functions of redshift and z-band absolute magnitude, color coded by their Sérsic index (ns). All parameters were extracted from the NSA catalog, without any additional processing. As expected from the implicit diameter selection of both the primary and secondary subsamples, there is a trend of selecting intrinsically brighter galaxies at higher redshifts than at lower ones (e.g., Walcher et al. 2014). This is reflected in the prevalence of earlier-type, redder (higher ns), and, in general, more massive galaxies at larger cosmological distances. Two trends with redshift, created by the diameter selection of the subsamples, are clearly seen in the left panel of the figure. The wide redshift range covered by the MaNGA survey in comparison with other IFS galaxy surveys (e.g., PHANGS or CALIFA; Sánchez et al. 2012; Rosolowsky et al. 2019) is worth noticing, although it is slightly narrower than the range covered by other surveys, like SAMI (Croom et al. 2012). Then, (earlier-type/more massive) higher-redshift galaxies are not observed at exactly the same cosmic epoch as (later-type/less massive) lower-redshift ones. As stated in previous studies (e.g., Ibarra-Medel et al. 2016; Sánchez et al. 2019a), this is relevant when exploring galaxy properties that present a clear cosmological evolution (e.g., age, metallicity, star formation rate (SFR), etc.). Although this feature is not unique to the MaNGA sample selection, it is particularly relevant in our study, because the MaNGA redshift range corresponds to ∼1.2 Gyr of cosmic time. For the same reason, galaxies at different redshifts are not observed at the same physical resolution, as will be discussed later on. For some particular studies, it is recommended to perform a thoughtful redshift selection, to avoid spurious results (Barrera-Ballesteros et al. 2022). Another interesting feature is that the galaxy distribution in the color–magnitude diagram of Figure 1 is smoother than usual: the GV and the bimodality between the red sequence and the blue-cloud galaxies is less marked. This is a consequence of the addition of the color-enhanced subsample. As discussed below, this behavior disappears once we apply the proper volume correction.
Figure 1. Distribution of galaxies in the MaNGA sample, in the color–redshift diagram (left panel) and the color–magnitude diagram (right panel). Each solid dot corresponds to one of the analyzed objects, color coded by its Sérsic index. The contours represent the density distribution, encircling 90%, 65%, and 40% of the objects, respectively. Density distributions of the full sample (black lines) and the segregated in bins of Sérsic index (colored lines) as a function of redshift, absolute magnitude, and color are represented at the upper and right edges of the panels. All parameters were extracted from the NSA catalog.
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Standard image High-resolution image3. Data
All galaxies were observed using the MaNGA IFU (Drory et al. 2015), attached to two double-arm twin spectrographs (Smee et al. 2013), which allowed coverage of the wavelength range between ∼3600 and 10000 Å, with a resolution of R ∼ 2000. The IFU system comprises 17 fiber bundles of different sizes (12''–32''/diameter) and numbers of fibers, each of them following a hexagonal pattern. Each set of fiber bundles is connected to a plate chart located at the focal plane of the telescope, which covers the entire FOV of the 2.5 m SDSS telescope (∼1°; Gunn et al. 2006), following a configuration that optimizes the number of observed galaxies at each visited location. In this way, each plate defines a set of observed objects, and each observed cube is therefore unambiguously defined by the combination of the plate number, the number of fibers in the bundle (19, 37, 61, 91, or 127), and an index defining the bundle number (01, 02, ...). The two latter numbers are combined into a single index, named ifudsgn (e.g., 12701). In summary, each cube is identified by the combination manga-plate-ifudsgn (e.g., manga-7443-12701), which will be used as the primary index in this article.
In order to cover the gaps between the adjacent fibers in each bundle, observations were performed following a minimum of three dithering exposures with fixed exposure times, a procedure adopted in previous IFS surveys (Sánchez et al. 2012). Since the survey strategy required reaching a certain minimum depth for all targets (Yan et al. 2016a), the same field/plate could be revisited several times during the survey to achieve this goal. Individual visits were then combined, and the final reduced data set comprises just a single frame for each plate and IFU, irrespective of the number of times that it was (re)observed. In addition to the science IFUs, each plate includes a set of 12 micro-IFUs, comprising seven fibers that point toward field stars, as well as a total of 15 sky fibers. These additional observations are used in the flux-calibration and sky-subtraction procedures, described in detail in Yan et al. (2016b) and Law et al. (2015), respectively.
Data reduction was performed using version 3.1.1 of the MaNGA Data Reduction Pipeline (DRP; Law et al. 2016). This package comprises the usual steps in the reduction of fiber feed IFS data (Sánchez 2006), including: (i) tracing the locations of the spectra corresponding to each individual fiber in the CCD; (ii) the extraction of these spectra; (iii) wavelength calibration; (iv) the homogenization of the spectrophotometric transmission of each individual fiber (fiberflat); (v) sky subtraction; (vi) the combination of the several dithering exposures, reobservations of the same plate, and resampling of the data into a regular grid data cube (a step that requires correcting for differential atmospheric refraction, when needed); and (vii) flux calibration. The result of the data reduction for each combination of plate and ifudsgn is a single data cube, in which the x-axis and y-axis correspond to the spatial coordinates (R.A. and decl.), and the z-axis to the spectral information. The flux intensity at each spatial location and wavelength is stored in each (X,Y,Z) entry in this 3D array or cube. In this way, each channel in the z-axis corresponds to a monochromatic image, and each pixel at location (X,Y) comprises a single spectrum (for this reason, the pixels in IFU data cubes are known as spaxels, i.e., spectral pixels). The current version of the DRP provides three different versions of the reduced data: row-stacked spectra (plus a position table), comprising the individual spectra before resampling to a final data cube, and two versions of the data cubes, one with a logarithmic sampling of the spectral range (i.e., Δz = Δλ/λ) and another with a linear sampling (Δz = Δλ). Throughout this paper, we will use the linear sampling version of the data cube. Additional information provided by the DRP includes: (i) an error cube, comprising the propagated error associated with each spectrum at each location in the (X,Y) plane; (ii) a mask of the bad pixels (cosmic rays, CCD problems, etc.); (iii) a cube comprising the spectral resolution at each location and wavelength; and (iv) additional information, such as reconstructed broadband images at different bands or the Galactic dust extinction at the location of each target. The delivered products of the DRP are stored in a single FITs file, with a different extension containing each product, and a header that includes relevant information regarding the observing procedure (atmospheric conditions, number of visits, etc.).
Prior to any analysis, we perform preprocessing on the linear wavelength sampling data cubes provided by the MaNGA DRP. This procedure transforms the original data to the format needed by our analysis pipeline, a FITs file with at least three extensions: (i) one data cube comprising the spectral information (flux intensity), corrected by Galactic extinction, in units of 10−16 erg s−1 Å−1 cm−2, with the wavelength calibrated in a linear step with a normalized spectral resolution at FWHM; (ii) one data cube comprising the error in the flux intensity, in the same format and units as the first extension; and (iii) one data cube comprising the mask of bad pixels, in the same format as the two previous extensions. The Galactic extinction correction was performed using the dust extinction in the V-band (AV), estimated from the E(B − V) parameter included in the header of the original MaNGA data cube (EBVGAL keyword; extracted from Schlegel et al. 1998), adopting a canonical value of RV = 3.1 for the Milky Way, and the Cardelli et al. (1989) extinction law. The wavelength resolution was normalized to a value of FWHM = 3.7 Å, by convolving each spectrum at each wavelength with a Gaussian function comprising the differential resolution at the wavelengths where the original resolution is better than this value. This imposes a small degradation of the spectral resolution, according to Figure 18 of Law et al. (2016), at the advantage of not requiring the normalization of the resolution in each step of the spectral fitting procedure (as we discuss later on).
4. Analysis
The final MaNGA v3.1.1 data set comprises 11,273 unique data cubes. Of them, 10,245 correspond to unique data cubes of objects with redshift in the NSA catalog (i.e., galaxies). As indicated before, the same galaxy could be observed using a different combination of plate and IFU, which would correspond to a different data cube. As listed in Appendix E, a total of 44 galaxies were observed twice (corresponding to 88 data cubes). In this section, we describe the analysis performed on this full data set.
4.1. Summary of Pipe3D
The analysis is performed using a new implementation of the Pipe3D pipeline (Sánchez et al. 2016a), fully transcribed to python (pyPipe3D; Lacerda et al. 2022). This version of the code uses similar algorithms and the same analysis sequence as the previous version, adapted to make use of the unique computational capabilities of the new adopted coding language, improving its performance, and correcting bugs when needed. Pipe3D has been extensively used to analyze IFS data of very different natures, in particular data from the CALIFA (e.g., Cano-Díaz et al. 2016; Espinosa-Ponce et al. 2020), MaNGA (e.g., Ibarra-Medel et al. 2016; Barrera-Ballesteros et al. 2018; Sánchez-Menguiano et al. 2019; Sánchez et al. 2019a; Bluck et al. 2020), and SAMI surveys (e.g., Sánchez et al. 2019b), as well as individual (Sánchez et al. 2015) and large MUSE data sets (Sánchez-Menguiano et al. 2018; López-Cobá et al. 2020). It has been described in detail in previous articles (e.g., Sánchez et al. 2016a, 2016b, 2021), and thoroughly tested using both ad hoc simulations and mock data sets based on hydrodynamical simulations (Guidi et al. 2018; Ibarra-Medel et al. 2019). To avoid unnecessary repetition, we here provide a very brief summary, emphasizing the few novelties of the new code.
In summary, the code separates the stellar component and the ionized gas line emission in each spectrum, by fitting the former with a combination of simple stellar population (SSP) spectra. Prior to this decomposition, the SSP spectra are shifted to account for the stellar velocity (vel⋆) and convolved with a Gaussian function to account for the velocity dispersion (σ⋆). Finally, the SSPs are dust attenuated, adopting the Cardelli et al. (1989) extinction law. The parameters defining the kinematics (vel⋆ and σ⋆) and the intrinsic dust extinction (AV,⋆) are estimated using a limited set of SSP templates, which restrict the spaces of the parameters to avoid or limit the intrinsic degeneracies between these parameters and the intrinsic properties of stellar populations (e.g., metallicity/dispersion; Sánchez-Blázquez et al. 2011). This two-step procedure and its benefits are described in detail in Sánchez et al. (2016a) and Lacerda et al. (2022). The stellar decomposition provides the light fraction or weight (w⋆,L ) contributed by each SSP to the problem spectrum at a fixed spectral range (5450–5550 Å, similar to the V-band central wavelength), in addition to the vel⋆, σ⋆, and AV,⋆ parameters obtained in the first step. Each observed spectrum (Sobs) is fitted to a model spectrum of the stellar component (Smod), following the expression
![Equation (1)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn1.gif)
where Sssp is the spectrum of each SSP in the template library, E(λ) is the adopted extinction law, and G(vel⋆, σ⋆) is the Gaussian function describing the line-of-sight stellar velocity distribution.
After subtracting the best stellar population model from the observed spectrum, the fitting algorithm models the ionized gas emission with a set of individual Gaussian functions that are fitted to a set of predefined emission lines at known wavelengths, shifted by the observed velocity of the gas component. In this way, we derive the flux intensity (Fel), velocity (velel), and velocity dispersion (σel) for each emission line (el). The procedure is repeated iteratively, with the emission-line analysis being performed after each step in which the stellar population spectrum is modeled and subtracted from the original spectrum. The figure of merit used to define the best-fitting model (a reduced χ2) takes into account both the stellar and emission-line models provided by the full analysis just described. In summary, our procedure involves a combination of brute-force exploration of the nonlinear parameters within a limited range (vel⋆, σ⋆, and AV,⋆ for the stellar population, and vel and σ for each emission line), together with a pure linear inversion, to derive the weights (wssp) of the decomposition of the stellar population, plus an iterative automatic selection of the SSP templates to be included in the decomposition, based on the results of the previous fit.
The procedure just outlined is then applied to each data cube. However, it is not applied spaxel by spaxel. The results from simulations indicate that a minimum signal-to-noise ratio (S/N) is required for the fit to the stellar population to provide reliable results (Sánchez et al. 2016b). We thus perform a spatial binning, known as continuum segmentation (CS; described in Sánchez et al. 2016a), which groups adjacent spaxels, whose S/Ns are below a defined threshold, to produce a higher-S/N average spectrum. In this procedure, contrary to other ones found in the literature (e.g., Cappellari & Copin 2003), we do not group the spectra of adjacent spaxels if their intensities differ by more than a predefined percentage. In this way, the spatial shape of the original galaxy is preserved (to some extent), while the S/N is increased (although it does not always reach our goal value). From this analysis, we recover the parameters of the stellar population for each spatial bin (tessella or voxel), i.e., the weights of the decomposition of the SSPs (see Section 5.1.2 below), the kinematic parameters, and the dust extinction, together with the best spectral model. The model spectrum for each tesella is then scaled to the flux intensity of each spaxel, by using the so-called dezonification parameter (DZ; Cid Fernandes et al. 2013), which is the ratio between the flux intensity of each spaxel with respect to the average value in the tesella in which this spaxel was grouped. Restricting this procedure to model the stellar spectrum, we obtain a model cube of the stellar population. Subtracting this model from the original cube, we obtain the so-called pure-GAS cube, a cube comprising only the information of the ionized gas emission lines (plus noise and residuals from the imperfect subtraction of the stellar component).
The pure-GAS cube is then analyzed using two different procedures. In the first one, a limited set of strong emission lines are fitted with individual Gaussian functions for each individual spaxel, providing a set of maps with the flux velocity and the velocity dispersion of each fitted emission line, and their corresponding errors (Section 5.1.4). In a second step, a much larger set of emission lines is analyzed using weighted moment analysis, deriving maps of the three parameters indicated before (Fel, velel, and σel), together with the spatial distribution of the equivalent width (EWel) of the considered emission line, el (Section 5.1.5). Two main differences have been introduced into the analysis of the emission lines with respect to previous analyses of MaNGA data using Pipe3D (Sánchez et al. 2018). (i) The Gaussian fitting is now performed in two steps. The first step replicates the brute-force exploration of the range of parameters described in Sánchez et al. (2016b). This exploration avoids falling into a local minimum. However, it is not very accurate. A second step is introduced by executing a Levenberg–Marquardt minimization algorithm, which uses the results of the first exploration as a guess. This additional fitting significantly increases the accuracy and precision of the results (Lacerda et al. 2022). (ii) A new and larger set of emission lines, with improved definitions of wavelength, has been adopted for the weighted momentum analysis, as will be explained below. The former version of the emission-line treatment has been performed as well, for a simpler comparison with previous results.
Finally, the pipeline obtains the spatial distribution (map) of a set of stellar spectral indices (Section 5.1.3). To do this, we subtract the best model for the emission lines from the spectrum of each tessella, in order to generate a stellar spectrum without contamination by the ionized gas contribution. Then, for each stellar spectral index, we derive its equivalent width by defining a wavelength range at which we measure the median flux intensity, and two adjacent wavelength ranges at which the continuum is estimated. We adopt the definitions of the different stellar indices and the procedures described in Cardiel et al. (2003). We note that we depart from the classical definition of D4000, derived using the flux intensity in units of frequency (i.e., Fν ; Bruzual 1983), and adopt the more convenient functional form proposed by Gorgas et al. (1999; their Equation (2)). As in the previous cases, the pipeline provides maps of the spectral index and its estimated error.
The errors provided by Pipe3D are based on a set of Monte Carlo (MC) iterations, in which the original spectrum is perturbed within the errors (a needed input in the preprocessed data cubes; see Section 3). Every time a stellar or an emission-line model is subtracted from the original spectrum, the uncertainties in the model are also propagated into the errors. The final errors thus include both the noise level and the uncertainties in the modeling of the galaxy component. More details of this procedure are given in Lacerda et al. (2022).
4.2. Adopted Stellar Library
One of the major changes with respect to the previous versions of the delivered data products, besides the use of an improved and transcribed version of the code, is the use of a new SSP spectral library in our analysis. For SDSS DR14 and DR15, we delivered versions v2_1_2 and v2_4_3 of the MaNGA data products, produced by adopting the GSD156 SSP library (Cid Fernandes et al. 2013). This library resulted from the combination of two sets of SSP model spectra. For stellar populations older than 65 Myr, the GSD156 library uses synthetic spectra from Vazdekis et al. (2010) and Falcón-Barroso et al. (2011), based on the MILES empirical stellar library (Sánchez-Blázquez et al. 2006). For stellar populations younger than 65 Myr (not included in the cited models), GSD156 uses synthetic spectra from the GRANADA library (González Delgado et al. 2005; Martins et al. 2005), based on theoretical stellar spectra. We adopted the Salpeter (1955) Initial Mass Function (IMF) for stellar masses between 0.1 and 100 M⊙. GSD156 comprises 156 SSP spectra, sampling 39 ages from 1 Myr to 14 Gyr (on a near logarithmic scale), and four metallicities (Z/Z⊙ = 0.2, 0.4, 1, and 1.5).
For the current implementation of the code, we adopt a completely different library, which we call MaStar_sLOG. This SSP library uses the recently delivered MaNGA stellar library (MaStar; Yan et al. 2019), which includes 8646 spectra for 3321 unique stars. This is a considerable increase in the number of stars and range of sampled atmospheric properties over the previously adopted stellar library (MILES comprises a total of ∼1000 stars). In Appendix A, we present a summary of the updated set of the Bruzual & Charlot (2003; hereafter,BC03) stellar population synthesis models that use several stellar libraries, including MaStar. The full MaStar SSP library comprises a total of 3520 individual spectra, covering 220 ages and 16 metallicities (assuming a solar [α/Fe] ratio and a solar metallicity of [Fe/H] = 0.017). This library is far too impractical to be applied to a stellar decomposition technique like the one performed by pyFIT3D, outlined in Section 4.1. Thus, we experiment with different combinations of SSPs extracted from this full library, adopting linear, logarithmic, and mixed samplings of both age and metallicity, comparing the results with previous ones and with theoretical expectations. In all cases, we repeat the full analysis described in this article for a subset of the full MaNGA data set, comprising 9500 data cubes (the so-called MPL-10 internal release). A detailed description of the experiments and their results will be presented elsewhere (S. F. Sánchez 2022, in preparation). In summary, we conclude that a sampling in age and metallicity, in which the steps between consecutive values increases are multiplicative (i.e., a pseudologarithmic sampling), results in a good compromise between: (i) an adequate sampling of the spectral properties; (ii) an efficient exploration (in terms of computing time); and (iii) the estimation of accurate and precise results. We must recall that, based on previous experiments (Sánchez et al. 2016b), an arbitrary increase of the sampling of the stellar parameters by an SSP library is not feasible for data with limited S/Ns. The finally adopted MaStar_sLOG library was built following this scheme and comprises 273 SSP spectra, sampling 39 ages from 1 Myr to 13.5 Gyr, and seven metallicities (Z = 0.0001, 0.0005, 0.002, 0.008, 0.017, 0.03, and 0.04) or (Z/Z⊙ = 0.006, 0.029, 0.118, 0.471, 1, 1.764, and 2.353), as indicated in Tables 4 and 8.
4.3. Morphological Classification
Morphology is known to be a fundamental property that affects (and is affected by) galaxy evolution, being directly connected with the dynamical stage, the stellar content, the star formation stage, the gas content, and even the presence of active galactic nuclei. Therefore, it is essential to have a morphological classification of the galaxies as a basis for comparing with the properties derived by our analysis. The catalogs available publicly, which are based on visual classifications of galaxy morphology, only include the galaxies in the releases up to DR15, comprising ∼4700 objects, less than half the galaxies available in the final sample. For the purposes of this article, we do not need a highly accurate determination of the morphology of each individual galaxy. It is enough to have a consistent classification in terms of statistical properties. As such, we use the accurate visual morphology determinations from the SDSS VAC by Vázquez-Mata et al. (2022), which includes ∼6000 galaxies and is an extension of the publicly available SDSS VAC,
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to obtain training and testing samples for a machine-learning algorithm to classify the rest of the galaxies in the full sample. We select features that are expected to be informative of morphological class, namely: the Sérsic index, the NSA stellar mass, the line-of-sight velocity-to-velocity dispersion ratio at the effective radius, the ellipticity, the concentration index, and the colors (extracted from the NSA catalog). For such a task, we implemented a Gradient Tree Boosting algorithm, which is a type of ensemble method that successively trains a predefined number of decision trees, each improving upon its predecessor errors. We found the resulting classification to be satisfactory for our purposes: the deviation between the estimated morphology and that of the VAC for the testing sample is consistent with the deviation observed between this VAC and other morphological classifications (discussed below). We will detail this method in a forthcoming publication (A. Mejía-Narváez et al. 2022, in preparation).
4.4. Photometric and Structural Properties
We estimate a set of photometric and structural properties directly from the MaNGA data cubes in addition to the different parameters derived by pyPipe3D. Among them, we compute the broadband photometry in the Gunn u, g, r, and i and the Johnson B, V, and R filters, adopting the Vega photometric system and using the filter parameters provided by Fukugita et al. (1995), redshifted to the rest frame of each object. Thus, no additional corrections, such as K or E corrections, have to be considered. From this photometry, we derive each galaxy's observed and absolute magnitudes, again using the redshift to estimate the cosmological distance. 12 In addition, we estimate the radii R50 and R90, which include, respectively, 50% and 90% of the integrated flux in the V band inside the MaNGA FOV, and the concentration index R90/R50. In the literature, there are several estimates of similar properties derived for the MaNGA galaxies using direct imaging, like the ones provided by SDSS (Blanton et al. 2017; Fischer et al. 2019) or the DESI survey (Arora et al. 2021). However, they present some disadvantages and obvious intrinsic differences: (i) none of them can derive a direct rest-frame estimate of the photometry, requiring a K correction based on the modeling of a spectral energy distribution to obtain them; (ii) the same limitation affects the structural properties, such as R50 or the concentration index; and (iii) these photometric values are usually derived for the full (optical) extension of the galaxies, and not limited to the FOV of the IFS data. This is very useful for deriving the global integrated galaxy properties (e.g., stellar mass or absolute magnitude). However, they are not appropriate for comparison with aperture-limited quantities, as derived by the pyPipe3D analysis.
From our photometry, we estimate each galaxy's stellar mass from its M/L ratio, assuming the relation
![Equation (2)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn2.gif)
of Bell & de Jong (2000), valid for the Chabrier (2003) IMF, using (ϒB−V ) as the B − V color and our V-band absolute magnitude. These stellar-mass estimates are considered to be less accurate, but most probably more precise, than estimates based on the spectroscopic analysis performed by pyPipe3D. We acknowledge that there are more recent ϒcolor derivations (e.g., Zibetti et al. 2009; Taylor et al. 2011; Zhang et al. 2017; García-Benito et al. 2019), some of them based on IFS data, and more precise estimates of the stellar mass that take into account multiband photometry (e.g., Arora et al. 2021). However, we adopt Equation (2) for easy comparison with previous calculations (Sánchez et al. 2016a). Nevertheless, since we provide colors and absolute magnitudes, it should be easy to define other ϒcolor stellar-mass estimators.
Finally, we derive each galaxy's V-band surface brightness at its effective radii R50 and Re, encircling half of the light within the FOV and half of the total integrated light of the galaxy, respectively, by averaging the flux values in an elliptical ring (using the known position angle and ellipticity of the object) of width 0.15 (in units of Re or R50). Again, the surface brightness is measured in the galaxy rest frame, which is not directly accessible when using broadband imaging. As mentioned above, parameter errors are estimated from MC iterations, perturbing the observed spectra using the error spectra included in the MaNGA data cubes.
4.5. Quality Control
The analysis by pyPipe3D runs in an automatic way through the entire MaNGA data set, without human supervision. Therefore, we must perform a quality control on the results, to identify and flag any possible issues relating to either the data or the analysis itself. This procedure comprises different steps. First, an automatic exploration is done to check that the analysis has produced all the required files, and that they are of the required format with the expected values (i.e., no map or table is filled with NaN, Inf, or zeros). Then, a second automatic exploration is done, by comparing two basic parameters derived from our analysis (the redshift and the integrated stellar mass) with those provided by the NSA catalog. If significant differences (≳30%) are found in any of these quantities, the corresponding data cube is flagged with a "WARNING."
In addition, a more detailed by-eye exploration is performed to determine the quality of the data. In doing so, we created an interactive web page on which we can show for each galaxy/data cube: (i) its central spectrum, corresponding to an aperture of 25, together with the best-fitting model provided by pyPipe3D; (ii) the mass assembly and chemical enrichment curves (e.g., Pérez et al. 2013; Ibarra-Medel et al. 2016; Camps-Fariña et al. 2021); (iii) three true color images generated using (a) the original SDSS u-, g-, and r-band images; (b) the same image generated using synthetic broadband images extracted from the original MaNGA data cubes for the same filters; and (c) the emission-line maps for [O iii]5007 (blue), Hα (green), and [N ii]6584 (red); (iv) maps of the luminosity-weighted (LW) age (
), metallicity (
), and dust extinction, derived as part of the pyPipe3D process (described in detail in Section 5.1.1); and (v) a set of spatially resolved emission-line diagnostic diagrams, as described in López-Cobá et al. (2020), including (a) the WHAN diagram (Cid Fernandes et al. 2010); (b) the classical Baldwin–Phillips–Terlevich (BPT; Baldwin et al. 1981) diagrams involving the [O iii]/Hβ line ratio as a function of the [N ii]/Hα, [S ii]/Hα, and [O i]/Hα ones; (c) the spatial distribution of the emission-line velocity dispersion, the [N ii]/Hα ratio, and the distribution of one as a function of the other; and (d) the gas and stellar velocity maps and their difference.
Figures 2, 3, and 28 (Appendix C) illustrate an example of the quality control analysis for a showcase data cube (manga-7495-12703). We search for: (i) evident problems in the quality of the explored spectra or issues in the fitting process; (ii) issues with the observations themselves (large masked areas and/or strong contamination by foreground stars); and (iii) the presence of strong AGNs or clear merging systems that may affect the stellar population and/or the kinematic analysis (just to warn the user). The visual quality control was performed by seven different people, who explored a subsample of the full data set, with an average overlapping of three different inspections per target/data cube. When there was a different appreciation of the quality by different inspectors, we adopted the worst-reported quality control flag.
Figure 2. An example of the information explored in the quality control process for the galaxy/cube manga-7495-12703. The top panel shows the central 5''/aperture spectrum of this galaxy (solid black line), together with the best-fitting model (solid yellow line), using the SSP template adopted in the derivation of the nonlinear parameters of the stellar population (Section 4.1 of Lacerda et al. 2022), and the residual of the original spectrum from the subtraction of this model. The gray and blue shaded areas correspond to masked regions along the full fitting process and in the first iteration of the analysis before subtracting the emission lines, respectively. The coefficients of the decomposition in this SSP library, i.e., the fraction of light at the normalization wavelength for each template in the library (wssp in Equation (1)), are shown in the bottom panel as a heatmap for the four ages and three metallicities of the considered library. The nonlinear parameters (systemic velocity, velocity dispersion, and dust extinction) and the LW and MW age and metallicity are indicated in the legend at the top right.
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Standard image High-resolution imageFigure 3. An example of the information explored in the quality control process for the galaxy/cube manga-7495-12703. The panels on the left comprise, from the top left to the bottom right: (i) a true color image created using the SDSS u-, g-, and r-band images; (ii) the same image created using images in the same bands synthesized from the MaNGA data cube; (iii) a similar true color image created using the [O iii]5007 (blue), Hα (green), and [N ii]6584 (red) flux intensity maps, together with the (iv) , (v)
, and (vi) AV,⋆ maps, all of them derived as part of the pyPipe3D process. The panels on the right show the normalized mass assembly (top) and the metallicity enrichment history (bottom) for this galaxy at different galactocentric distances (indicated with different colors), estimated from the pyPipe3D analysis following the procedures described in Camps-Fariña et al. (2021, 2021). We remark that no S/N mask has been applied to the data, so, therefore, the results corresponding to the outer regions in all panels are highly unreliable (e.g., the apparent increases of
and
at the edge of the FOV and/or the metallicity history beyond 2 Re).
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Standard image High-resolution imageThe reported quality control flags (QCFLAG) are listed in Table 1, indicating the level of importance ("OK," "BAD," or "WARNING"). Of the 10,245 cubes corresponding to galaxies analyzed by pyPipe3D, the code fails to analyze 25 cases, due to different issues with the data (very low S/Ns in most cases and evident empty fields). Of the remaining 10,220 cubes, in six cases we flagged our results as BAD (for the reasons described before), meaning that they should be discarded. In addition, 386 cubes had different issues, and we strongly recommend not using them without paying attention to the visual inspection of the original spectra, the results from the fitting, and, in particular, the nature of the warning. In summary, we provide good-quality analyses for 9828 data cubes (corresponding to 9784 individual galaxies).
Table 1. Quality Control Flags
QCFLAG | Level | Meaning |
---|---|---|
0 | OK | All QC steps passed |
1 | BAD | Wrong redshift |
2 | BAD | Low S/N or empty field |
3 | WARNING | Possible issue with the fitting and/or presence of a strong AGN |
4 | WARNING | Mass based on pyPipe3D does not match NSA reported value |
5 | WARNING | Redshift based on pyPipe3D does not match NSA reported value |
6 | WARNING | Bright foreground field star |
7 | WARNING | Evident merging system |
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5. Results
All the results from the analysis described in the previous sections are delivered through the network web pages. 13 For each individual MaNGA cube/galaxy, we release a single FITs file using the Pipe3D data model described below. In addition, a catalog including the individual properties of each galaxy is available too. Its format is described in the upcoming sections.
5.1. Pipe3D Data Model
The results from the analysis described above for each individual spectrum are a set of parameters derived for each particular spaxel. Each of these parameters is stored as a 2D array or map of the given quantity, which preserves the World Coordinate System (WCS) of the original data. These maps are the the final products of the analysis. For an optimal (compact and easily accessible) distribution of the products, we pack them in data cubes, i.e., 3D arrays in which each channel (slide on the z-axis) corresponds to one of the maps described before and, therefore, to one of the derived parameters. Instead of storing all the products in a single data cube, they are packed into a set of data cubes based on a physically motivated association of parameters. In the current implementation of Pipe3D, there are five data cubes: (i) SSP, which contains maps of the spatial distribution of the main properties of the stellar populations; (ii) SFH (star formation history), which comprises the distribution of the light fraction derived by the stellar decomposition for each SSP in our template library; (iii) INDICES, which comprises the maps of the stellar indices; (iv) ELINES, which includes the properties of a set of strong emission lines derived by the Gaussian fitting procedure; and (v) FLUX_ELINES, which includes the properties of a much larger set of emission lines, based on the weighted moment analysis. In the current release, we distribute two versions of the ELINE analysis, based on two different sets of emission lines, with the new version labeled FLUX_ELINES_LONG. Finally, all the data cubes are stored as individual extensions of a single FITs file (the Pipe3D file). In this release, we include, in addition to these six extensions: (i) a primary extension with the header of the original MaNGA cube (ORG_HDR); (ii) a mask of the brightest field stars recovered from the Gaia survey (GAIA_MASK); and (iii) a mask of the spaxels within the hexagon with a high enough S/N to provide reliable estimates of the stellar population properties (SELECT_REG).
Table 2 summarizes the structure of the Pipe3D FITs file. Each file can be easily associated with its corresponding MaNGA cube from the file naming convention, i.e., manga-plate-ifudsg.Pipe3D.cube.fits, where plate and ifudsg correspond, respectively, to the number of the physical plate used during the pointing and the IFU bundle design and number. These two numbers uniquely define a fixed pointing to a certain galaxy, as indicated before. In this way, manga-7495-12704.Pipe3D.cube.fits corresponds to the MaNGA observation of a particular galaxy, observed using plate number 7495, and the fourth IFU (04), comprising 127 fibers. It is important to note that one galaxy may be observed using different plates and IFUs, corresponding to repeated observations that have been treated or analyzed individually, as if they were different objects. In the final MaNGA distribution, there are 90 of these repeated galaxies (i.e., 2 × 45 cubes). Since they are different observations, taken under different atmospheric conditions, and calibrated using different stars, they offer a unique opportunity to evaluate the quality of the data. We discuss them in detail in Appendix E.
Table 2. Description of the Pipe3D File
HDU | EXTENSION | Dimensions |
---|---|---|
0 | ORG_HDR | () |
1 | SSP | (72, 72, 21) |
2 | SFH | (72, 72, 319) |
3 | INDICES | (72, 72, 18) |
4 | ELINES | (72, 72, 11) |
5 | FLUX_ELINES | (72, 72, 456) |
6 | FLUX_ELINES_LONG | (72, 72, 1536) |
7 | GAIA_MASK | (72, 72) |
8 | SELECT_REG | (72, 72) |
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5.1.1. SSP Extension
As indicated before, this extension comprises a data cube with the average properties of the stellar populations derived from the multi-SSP fitting procedure outlined in Section 4.1. The content of each channel with index N is described in the header entry named DESC_N, starting with N = 0 for the first channel, corresponding to the properties listed in Table 3. An example of the content of this extension, for data cube manga-7495-12704, is shown in Figure 4.
Figure 4. Example of the content of the SSP extension in the Pipe3D FITs file, corresponding to the MaNGA data cube (galaxy) manga-7495-12704. Each panel shows a color image of the property stored in the corresponding channel of the data cube, as listed in Table 3. The index, corresponding property, and units are indicated in each panel with the labels at the bottom right, bottom left, and top right.
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Standard image High-resolution imageTable 3. Description of the SSP Extension
Channel | Units | Stellar Index Map |
---|---|---|
0 | 10−16 erg s−1 cm−2 | Unbinned flux intensity at ∼5500 Å, fV |
1 | None | Continuum segmentation index, CS |
2 | None | Dezonification parameter, DZ |
3 | 10−16 erg s−1 cm−2 | Binned flux intensity at ∼5500 Å, fV,CS |
4 | 10−16 erg s−1 cm−2 | StdDev of the flux at ∼5500 Å, efV,CS |
5 | log10(yr) | LW age, ![]() |
6 | log10(yr) | MW age, ![]() |
7 | log10(yr) | Error of both ![]() |
8 | dex | LW metallicity, Z⋆,L |
In logarithmic scale, normalized to | ||
the solar value (Z⊙ = 0.017) | ||
9 | dex | LW metallicity, Z⋆,M |
In logarithmic scale, normalized to | ||
the solar value (Z⊙ = 0.017) | ||
10 | dex | Error of both Z⋆ |
11 | mag | Dust extinction of the st. pop., AV,⋆ |
12 | mag | Error of AV,⋆, ![]() |
13 | km s−1 | Velocity of the st. pop., vel⋆ |
14 | km s−1 | Error of the velocity, evel |
15 | km s−1 | Velocity dispersion of the st. pop., σ⋆ |
16 | km s−1 | Error of σ⋆, eσ |
17 | log10(M⊙ /L⊙) | Mass-to-light ratio of the st. pop., ϒ⋆ |
18 | log10(M⊙ /sp2) | Stellar-mass density per spaxel., Σ⋆ |
19 | log10(M⊙ /sp2) | Dust-corrected Σ⋆, Σ⋆,dust |
20 | log10(M⊙ /sp2) | Error of Σ⋆ |
Note. "Channel" indicates the z-axis of the data cube, starting from 0.
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The first channel comprises the median flux intensity in the natural units of the Pipe3D preprocessed MaNGA data cube (Section 3), at the wavelength range 5450–5550 Å. The second channel includes the index of the tessella in which each spaxel is grouped, after the binning has been performed to increase the S/N. The third channel comprises the dezonification map, as described in Section 4.1. The average flux intensity and the standard deviation within the same wavelength range as for the unbinned flux in the first channel, after applying the spatial binning, are included in the following two channels. Up to now, the stored properties correspond to parameters derived directly from the original data cubes, before performing the spectral fit. However, the remaining channels correspond to properties extracted directly from our fits, including the LW and mass-weighted (MW) age ( and
) and metallicity (
and
), respectively, as well as the dust extinction, the stellar velocity, the stellar velocity dispersion, the stellar mass-to-light ratio, and the stellar-mass density (with and without correction for dust extinction).
A detailed description of how these parameters are derived is presented in Sánchez et al. (2016b, Section 2.3) and Sánchez et al. (2020, Section 3.1, Equations (2)–(4)). The kinematic parameters (vel⋆ and σ⋆), and the dust extinction (AV,⋆), are derived directly from the first step of the spectral decomposition analysis described in Section 4.1, and expressed in Equation (1). The LW parameters (PL ) are derived from
![Equation (3)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn3.gif)
and the MW parameters (PM ) from
![Equation (4)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn4.gif)
where wssp,⋆,L is the light fraction/weight of each SSP in the library at the normalization wavelength derived from the spectral fit, according to Equation (1), Pssp is the value of the given parameter for the corresponding SSP (e.g., age, metallicity), and ϒssp,⋆ is the stellar mass-to-light ratio. Note that both the LW and MW average values are indeed geometrical averages (or averages of logarithmic parameters). Thus, these maps should be interpreted as a realization of the statistical distribution of the considered parameter (e.g., age or metallicity distributions; Mejía-Narváez et al. 2020).
Finally, the mass-to-light ratio is derived from a simple weighted average:
![Equation (5)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn5.gif)
The stellar surface density is then derived by multiplying ϒ⋆ by the observed surface density at the normalization wavelength (approximately the rest-frame V band), corrected for extinction by dust and luminosity distance (DL ), and divided by the area of each spaxel (asp):
![Equation (6)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn6.gif)
5.1.2. SFH Extension
This extension comprises a data cube where each slice/channel includes the spatial distribution of the light fraction (weights w⋆,L in Equation (1)) of each SSP within the adopted library at the normalization wavelength (5500 Å), following the WCS of the original MaNGA cube. Since the decomposition of the stellar population is done for the spatial binning described in Section 4.1, the distributed w⋆,L have the same value for all the spaxels within the same tessella. As indicated in Section 4.2, the adopted MaStar_sLOG library comprises 273 SSP spectra, covering 39 ages and seven metallicities (Table 4). The spatial distributions of the w⋆,L for each SSP are then stored in the first 273 channels of the SFH extension. Then, 39 additional channels (from N = 273 to N = 311, with the first channel corresponding to index N = 0) comprise the weights for each individual age (i.e., the values derived by coadding the seven w⋆,L for the same age, but different metallicities). An example of the spatial distribution of the latter weights for the same data cube as shown in Figure 4, manga-7495-12704, is shown in Figure 5. Since the weights w⋆,L are not weighted by the flux intensity, i.e., they are just relative quantities, the original shape of the galaxy is not evident in almost all of the panels. Only for the youngest ages (t < 30 Myr) is it possible to trace the location of the disk of this galaxy (somehow). Curiously, most of the light corresponds to stars with ages between ∼1 and ∼6 Gyr, which are more homogeneously distributed than younger stars. Finally, w⋆,L increases slightly for older stars toward the center, up to ∼12 Gyr. These weights/light fractions correspond to the spatially resolved age distribution function, which is usually explored in the analysis of resolved stellar populations (e.g., Hasselquist et al. 2020).
Figure 5. Example of the content of the SFH extension in the Pipe3D FITs file, corresponding to the MaNGA data cube (galaxy) manga-7495-12704. Each panel shows a color image with the fraction of light at the normalization wavelength (w⋆,L ) for the different age ranges included in the SSP library. The age range is indicated in the bottom left boxes, and the corresponding indices of the coadded channels in the SFH extension are shown in the bottom right legends. For clarity, we do not show the individual w⋆,L maps included in the SFH extension.
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Standard image High-resolution imageTable 4. Description of the SFH Extension
Channel | Description of the Map |
---|---|
0 |
w⋆,L
for (![]() |
272 |
w⋆,L
for (![]() |
273 |
w⋆,L
for ![]() |
311 |
w⋆,L
for ![]() |
312 | w⋆,L for Z⋆ = 0.0001 |
318 | w⋆,L for Z⋆ = 0.04 |
Note. "Channel" indicates the z-axis of the data cube, starting from 0, and w⋆,L
indicates the fraction (weight) of light at 5500 Å, corresponding to an SSP of: (i) a certain age () and metallicity (Z) (channels 0–272); (ii) a certain age, i.e., coadding all w⋆,L
corresponding to SSPs with the same age, but different metallicities (channels 273–311); and (iii) a certain metallicity, i.e., coadding all w⋆,L
corresponding to SSPs with the same metallicity, but different ages (channels 312–318). The adopted MaStar_sLOG SSP library comprises 39 ages,
/Gyr = (0.001, 0.0023, 0.0038, 0.0057, 0.008, 0.0115, 0.015, 0.02, 0.026, 0.033, 0.0425, 0.0535, 0.07, 0.09, 0.11, 0.14, 0.18, 0.225, 0.275, 0.35, 0.45, 0.55, 0.65, 0.85, 1.1, 1.3, 1.6, 2, 2.5, 3, 3.75, 4.5, 5.25, 6.25, 7.5, 8.5, 10.25, 12, and 13.5), and seven metallicities, Z⋆ = (0.0001, 0.0005, 0.002, 0.008, 0.017, 0.03, and 0.04).
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In addition, seven more channels are included in the SFH extension, comprising the weights for each individual metallicity (i.e., the values derived by coadding the 39 w⋆,L for the same metallicity, but different ages). Figure 6 shows the spatial distribution of these light fractions for each of the metallicities included in the SSP library. Despite the clumpy distribution, which is a consequence of the local uncertainties in the derivation of w⋆,L , there are clear patterns that emerge. For instance, the higher light fractions are more distributed in the outer regions of the galaxies for the lowest metallicities, while the distributions are more homogeneous for the highest metallicities. This is a clear consequence of the spatial variation of the metallicity distribution function (MDF; as reported by Mejía-Narváez et al. 2020). Indeed, these weights are the basic ingredients for deriving the MDFs using our analysis.
Figure 6. Similar to Figure 5, showing the w⋆,L for the different metallicities included in the adopted SSP library (indicated in the bottom left boxes), corresponding to the channels of the SFH extension indicated in the bottom right legends.
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Standard image High-resolution imageTable 4 summarizes the information included in each channel of the SFH cube. This information is included in the header with a set of keywords named DESC_N, indicating the content of channel N, with N running from 0 to 318. For completeness, the original file generated by pyPipe3D, from which the information in channel N was obtained, has been listed in the header as FILE_N.
5.1.3. INDICES Extension
This extension comprises a data cube in which each channel/slice corresponds to the spatial distribution of the stellar indices derived for the emission-line-subtracted spectra, following the procedure outlined in Section 4.1 and described in detail in Lacerda et al. (2022) and Sánchez et al. (2016b). As in the previous cases, this analysis has been done for the averaged spectra within each spatial bin, and therefore the reported values are the same for all the spaxels within the same tessella. The content of each channel/slice is listed in Table 5, indicating the explored stellar index and its error. The adopted spectral range for measuring each index, together with the ranges adopted to estimate the adjacent continuum (at bluer and redder wavelength ranges, with respect to the bandwidth that defines the index) are included in the table. For completeness, we include the average flux within the full wavelength range covered by the data and its standard deviation, as proxies for the signal and noise within each tessella. The corresponding information is stored in the header keywords named INDEXN, where N corresponds to each channel. Figure 7 shows an example of the content of this extension, corresponding to the same data cube shown in previous figures (i.e., manga-7495-12704; e.g., Figure 4) and including both the measurements and the estimated errors. As expected for a spiral galaxy, there are clear radial patterns in the different stellar indices that follow the light distribution: (i) D4000 presents a radial gradient, with the highest values being found in the center of the galaxy; (ii) all the explored Balmer indices (Hβ, Hγ, and Hδ), show a decline in the inner regions, most probably associated with the location of the bulge of this galaxy, and a rise, spatially coincident with the disk and the spiral arms; and (iii) the indices more sensible to stellar metallicity, in particular Mgb and Fe5335, present a mild decline from the center to the outer regions. The error maps show a near-constant distribution for all the indices, suggesting that the adopted spatial binning has indeed increased the S/Ns of the outer regions by adding the required spectra. Only in the outermost regions, at both the east and west edges of the FOV, do the errors rise up, suggesting that the surface brightness of the galaxy has dropped beyond the ability of the binning procedure to produce reliable (high enough S/N) spectra. Finally, the map of the standard deviation of the flux intensity presents higher values at the locations of the spiral arms, as expected, since in these regions the emission-line-subtracted spectra (used for the analysis of the stellar indices) present stronger variations and residuals. Similar patterns are appreciated for all the explored galaxies, with their own peculiarities and signatures.
Figure 7. Example of the content of the INDICES extension in the Pipe3D file, corresponding to the MaNGA data cube (galaxy) manga-7495-12704. Each panel shows a color image of the content of the channel in the data cube. The actual content is indicated in the lower left of each panel, the channel in the lower right, and the units of the represented quantity are given in the upper right legend. For the flux and flux error, the units are 10−16 erg s−1 Å− cm−2.
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Standard image High-resolution imageTable 5. Description of the INDICES Extension
ID | Channel | Units | Channel Content | Index λ Range (Å) | Blue λ Range (Å) | Red λ Range (Å) |
---|---|---|---|---|---|---|
Hd | 0/9 | Å | Hδ index/error | 4083.500–4122.250 | 4041.600–4079.750 | 4128.500–4161.000 |
Hβ | 1/10 | Å | Hβ index/error | 4847.875–4876.625 | 4827.875–4847.875 | 4876.625–4891.625 |
Mgb | 2/11 | Å | Mgb index/error | 5160.125–5192.625 | 5142.625–5161.375 | 5191.375–5206.375 |
Fe5270 | 3/12 | Å | Fe5270 index/error | 5245.650–5285.650 | 5233.150–5248.150 | 5285.650–5318.150 |
Fe5335 | 4/13 | Å | Fe5335 index/error | 5312.125–5352.125 | 5304.625–5315.875 | 5353.375–5363.375 |
D4000 | 5/14 | Å | D4000 index/error | 4050.000–4250.000 | 3750.000–3950.000 | |
Hdmod | 6/15 | Å | H![]() | 4083.500–4122.250 | 4079.000–4083.000 | 4128.500–4161.000 |
Hg | 7/16 | Å | Hϒ/error | 4319.750–4363.500 | 4283.500–4319.750 | 4367.250–4419.750 |
Flux | 8/17 | 10−16 erg s−1 cm−2 | Median flux/standard deviation a |
Notes. "Channel" indicates the z-axis of the data cube starting from 0.
a This is measured along the entire wavelength range covered by the spectroscopic data.Download table as: ASCIITypeset image
5.1.4. ELINES Extension
As outlined in Section 4.1, and described in detail in Lacerda et al. (2022) and Sánchez et al. (2016a), the pipeline explores the properties of the emission lines by performing two different analyses. In the first analysis, a set comprising the strongest emission lines in the optical range ([OII]3727, [O iii]4959,5007, Hβ, [N ii]6548,83, Hα, and [S ii]6717,31) is fitted with a single-Gaussian function for each line at each spaxel within the pure-GAS cube (once the best model spectra for the stellar populations are subtracted). The fitting procedure recovers the flux intensity, the velocity, and the velocity dispersion for each emission line. The pipeline stores the results of this analysis in the ELINES extension of the Pipe3D FITs file. As in the previous cases, this extension comprises a data cube, in which each channel/slice includes the spatial distribution of a different property derived from this fitting procedure, following the scheme described in Table 6. The corresponding information is stored in the header keywords DESC_N, where N corresponds to the channel index (starting with 0). An example of the content of this extension for data cube (galaxy) manga-7495-12704 is included in Figure 8. For this particular galaxy, the Hα velocity map included in the first channel shows a clear rotational pattern, with an S-shaped distortion in the central regions, suggesting the possible presence of a bar. The ionized gas presents a low velocity dispersion over all the disk near to the instrumental dispersion, rising only in the central regions, at the location of the bulge. The intensity maps of all the emission lines show a clumpy structure, as expected from ionized gas dominated by H ii regions and associations (at the spatial resolution of these data), with most of these regions being distributed along the spiral arms of this galaxy. This is the expected ionized gas distribution for a low-inclination spiral galaxy (e.g., Sánchez 2020; Sánchez et al. 2021).
Figure 8. Example of the content of the ELINES extension in the Pipe3D file, corresponding to the MaNGA data cube (galaxy) manga-7495-12704. Each panel shows a color image of the content of a channel from this data cube. The first panel corresponds to the velocity, in km s−1, and the second panel corresponds to EW(Hα), in angstroms. The remaining panels represent the distributions of the flux intensities for the different analyzed emission lines (lower left legend), in units of 10−16 erg s−1 Å− cm−2.
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Standard image High-resolution imageTable 6. Description of the ELINES Extension
Channel | Units | Description of the Map |
---|---|---|
0 | km s−1 | Hα velocity |
1 | Å | Hα velocity dispersion a |
2 | 10−16 erg s−1 cm−2 | [O ii]3727 flux intensity |
3 | 10−16 erg s−1 cm−2 | [O iii]5007 flux intensity |
4 | 10−16 erg s−1 cm−2 | [O iii]4959 flux intensity |
5 | 10−16 erg s−1 cm−2 | Hβ flux intensity |
6 | 10−16 erg s−1 cm−2 | Hα flux intensity |
7 | 10−16 erg s−1 cm−2 | [N ii]6583 flux intensity |
8 | 10−16 erg s−1 cm−2 | [N ii]6548 flux intensity |
9 | 10−16 erg s−1 cm−2 | [S ii]6731 flux intensity |
10 | 10−16 erg s−1 cm−2 | [S ii]6717 flux intensity |
Notes. "Channel" indicates the z-axis of the data cube, starting from 0.
a FWHM, i.e., 2.354σ. The instrumental velocity dispersion has not been removed.Download table as: ASCIITypeset image
5.1.5. FLUX_ELINES and FLUX_ELINES_LONG Extensions
The second analysis performed by pyPipe3D to extract the main parameters of the emission lines is based on a weighted moment analysis, summarized in Section 4.1 and described in detail in Lacerda et al. (2022) and Sánchez et al. (2016a). This analysis is performed over a larger list of emission lines than the Gaussian fitting procedure discussed in the previous section. For former distributions of the MaNGA Pipe3D analysis (DR14 and DR15; Sánchez et al. 2018), we adopted the list of emission lines included in Sánchez et al. (2016a), which was mostly focused on the emission lines detectable in the wavelength range bluer than λ = 7000 Å, since it was originally compiled to explore the ionized gas in the CALIFA data set (Sánchez et al. 2012). This list comprises a total of 56 emission lines. In this new distribution, we decided to update the list, enlarging the number of emission lines for better coverage of the redder wavelength range of the MaNGA data set, and using a single/homogeneous set of wavelengths (and not a compilation). We adopted the list of emission lines in Fesen & Hurford (1996), selecting those lines within the formal wavelength range covered by the MaNGA data set. The final list comprises 192 emission lines. For simpler compatibility and comparison with previous versions of the data set, we include in the Pipe3D file two extensions: one for analysis of the former list of emission lines (FLUX_ELINES), and another one for the new updated list (FLUX_ELINES_LONG). Both extensions follow the same basic scheme, described in Table 7, comprising eight different parameters for each analyzed emission line: flux intensity, velocity, velocity dispersion, and equivalent width (and their corresponding errors). The details of the two adopted sets of emission lines are presented in Appendix B, Tables 13 and 14. We note that in the ELINES and the two FLUX_ELINES extensions, the velocity dispersion is given as an FWHM in angstroms, without subtracting the instrumental dispersion. The following transformation should be applied to derive the velocity dispersion in kilometers per second:
![Equation (7)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn7.gif)
where c is the speed of light in kilometers per second, λ is the wavelength of the emission line, and σinst is the instrumental resolution (∼1.6 Å).
Table 7. Description of the FLUX_ELINES Extensions
Channel | Units | Description of the Map |
---|---|---|
I | 10−16 erg s−1 cm−2 | Flux intensity |
I+N | km s−1 | Velocity |
I+2N | Å | Velocity dispersion a |
I+3N | Å | Equivalent width b |
I+4N | 10−16 erg s−1 cm−2 | Flux error |
I+5N | km s−1 | Velocity error |
I+6N | Å | Velocity dispersion error |
I+7N | Å | Equivalent width error |
Notes. I is a running index over the set of emission lines listed in Appendix B, and N is the number of analyzed lines. N = 57 for FLUX_ELINES and N = 192 for FLUX_ELINES_LONG.
a FWHM, i.e., 2.354σ. The instrumental velocity dispersion has not been removed. b We follow the convention by which the EW for an emission line is negative and positive for an absorption line.Download table as: ASCIITypeset image
As for the previous extensions, we present in Figure 9 an example of the content of the FLUX_ELINES extension, corresponding to the properties derived for the [O ii]3727 emission line for the data cube (galaxy) manga-7495-12704. As expected, we observe a similar distribution in the flux intensity reported for this emission line as based on the moment analysis, rather than the one extracted using the Gaussian fitting (shown in Figure 8). The patterns in the velocity and velocity dispersion are also similar to the ones found for Hα using the Gaussian fitting, although they look noisier, as expected, since the [O ii]3727 emission line is weaker through the FOV in this galaxy, and it is a blended doublet. Finally, EW(Hα) presents clear negative values, with an absolute value larger than 3 Å through most of the FOV of this data set, in agreement with a scenario in which the ionization is dominated by young massive OB stars associated with recent star formation activity (e.g., Sánchez et al. 2021).
Figure 9. Example of the content of the FLUX_ELINES extension in the Pipe3D file, corresponding to the MaNGA data cube (galaxy) manga-7495-12704. Each panel shows a color image of the content of a channel from this data cube. For each panel, the actual content is indicated in the lower left, the channel number in the lower right, and the units of the represented quantity in the upper right legend. For the flux and flux error, the units are 10−16 erg s−1 Å−1 cm−2.
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Standard image High-resolution image5.1.6. GAIA_MASK Extension
Some data cubes have foreground field stars in their FOVs, which need to be masked in order to prevent artifacts from appearing in the estimations of the maps of the physical properties. The Gaia DR3 catalog was used to find the stars located within the FOV of each cube and mask them out. The Gaia DR3 catalog 14 (Gaia Collaboration et al. 2016, 2021) is the most complete photometric and astrometric survey of the full sky, with around 1.46 billion sources with a full astrometric solution. The astrometric information is useful, because it provides a way of identifying sources belonging to the Milky Way. The catalog was refined to include only sources with a measured parallax at least five times higher than their uncertainty. No further criteria for the selection of the Gaia stars were applied.
With this information, each cube has its coordinate range cross-checked with the Gaia catalog to find field star candidates. If there are any, the algorithm checks for the presence of the star by fitting a point-spread function (PSF) around the star coordinates. The fitted coordinates are then used to produce a mask image, as a circular aperture of 25 diameter is masked at the location of each star. This mask is stored in the GAIA_MASK extension of the Pipe3D FITs file. This procedure allows for a further check on the precision of the astrometry of the MaNGA data cubes, by comparing the Gaia coordinates to the fitted coordinates for the cube. We generally find good agreement, with typical discrepancies of ∼0
1.
5.1.7. SELECT_REG Extension
This extension comprises an additional mask image, masking all the spaxels within the FOV with a median S/N lower than 1 in the continuum emission within the wavelength range 5589–5680 Å. To do this, we use the get_SN_cube routine of the pyFIT3D package (Lacerda et al. 2022), which estimates the noise as the standard deviation of the flux intensity inside a given wavelength range. The flux standard deviation is derived for a data cube resulting from the subtraction from the observed spectra of a smoothed version of those same spectra, using a median filter size of six spectral pixels (i.e., 9 Å for the MaNGA data). This procedure removes the contribution of the shape of the spectra from the standard deviation, but not the contribution of the metallic features. Thus, the estimated error is an upper limit to the real one. Finally, the S/N map is calculated by dividing the mean value of the flux intensity by the estimated noise in the same wavelength for each spaxel.
5.2. A Practical Use of the Pipe3D Resolved Data Products
The described set of data products included in the current distribution comprises a vast data set that can be used to explore a large number of spatially resolved properties of galaxies. As a simple showcase, we use this data set to explore how the ionization conditions change across the optical extension of galaxies, as well as their dependence on galaxy properties. To do this, we emulate Figure 5 of Sánchez (2020), a review aimed at characterizing the main spatially resolved properties of galaxies in the nearby universe. This figure shows the distribution across the classical [O iii]/Hβ versus [N ii]/Hα diagnostic diagram of the full set of spatially resolved regions extracted from a compilation of data from four different IFS-GSs (CALIFA, SAMI, MaNGA, and AMUSSING++). This compilation comprises ∼8000 galaxies (i.e., it is of the same order as the final MaNGA data set), from which we select a subsample of ∼2000 well-resolved and well-sampled objects. Despite the size of this sample, and the efforts to homogenize the data products (for more details, see Sánchez 2020), no compilation has the advantages of a well-defined sample, like the one offered by MaNGA, considering that all the data were observed, reduced, and analyzed using the same procedures and tools. Following a similar philosophy, we select a subset of MaNGA data cubes with the best characteristics in order to explore the spatial distribution of the ionized gas. From the full sample, we retain only the galaxies/cubes observed with the largest IFUs (i.e., those comprising 127, 91, and 61 fibers), whose FOVs cover at least 2 Re of the galaxy. This guarantees good sampling and coverage of the spatially resolved properties, based on the simulations presented by Ibarra-Medel et al. (2019). Furthermore, we keep only those galaxies that are clearly resolved, i.e., whose Re is larger than the PSF FWHM of the MaNGA observations, following Belfiore et al. (2017b). Finally, we exclude highly inclined galaxies, i.e., the ones whose ellipticity is larger than 0.75. This latter condition is adopted to guarantee that the observed patterns result from changes with galactocentric distance, and are not an effect of a change of the ionization with vertical distance, due to the presence of shocks associated with galactic outflows (e.g., Heckman et al. 1990; Bland-Hawthorn 1995; López-Cobá et al. 2020) or extraplanar diffuse ionized gas (e.g., Flores-Fajardo et al. 2011; Levy et al. 2018). The final subsample fulfilling all these criteria comprises ∼5500 well-resolved/well-sampled galaxies/cubes.
Figure 10 shows the distribution along the BPT diagram of the full set of spatially resolved regions (spaxels) included in the final sample of well-resolved/well-sampled galaxies, segregated by mass and morphology, and color coded by the average EW(Hα). The average location within this diagram for different galactocentric distances is indicated by a solid circle (whose size grows with radius). To generate this figure, the contents of the FLUX_ELINES extensions in the pyPipe3D FITs file were used, to extract the flux intensity maps of [O iii]λ5007, Hβ, [N ii]λ6584, and Hα, and the map of EW(Hα). Then, for each galaxy, two maps were derived, each one comprising the two line ratios involved in the diagram. We use these maps to derive: (i) the azimuthally averaged radial distribution of both line ratios, with the galactocentric distance normalized to the effective radius; (ii) the density distribution of the spaxels across the BPT diagram; and (iii) the distribution of EW(Hα) across the same diagram. Finally, for each morphological and stellar-mass subsample (and for the complete sample), we derive the averages of all these properties. In this way, each galaxy contributes equally to the final distribution, irrespective of its number of ionized regions (represented by the individual density distribution).
Figure 10. Spatially resolved distribution of the subsample of 5624 well-resolved galaxies in the [OIII/Hβ] vs. [N ii]/Hα diagnostic diagram, segregated by mass (rows) and morphology (columns), and color coded by the average EW(Hα) at each location (color distribution). The contours indicate the density of the spaxels, with each consecutive contour encircling 90%, 50%, and 10% of the points. The solid gray circles correspond to the average locations in the diagram of spaxels at different galactocentric distances, with the sizes of the circles being proportional to their distance (ranging from 0.1 to 2.1 Re, in steps of 0.2 Re). In each panel, the solid and short-dashed lines correspond to the Kewley et al. (2001) and Kauffmann et al. (2003a) demarcation lines, which are frequently used to separate between SF- and AGN-like ionization. The long-dashed line corresponds to the separation between Seyferts and LINERs proposed by Kewley et al. (2001).
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Standard image High-resolution imageThe emerging patterns as seen in Figure 10 are very similar to those already discussed in Sánchez (2020), although there are some mild but evident differences that are clearly related to the differences in the explored samples. For the complete data set (the upper left panel), the peak of the density distribution traces the classical location of H ii regions (Osterbrock 1989), associated with recent SF processes. The highest densities are found at the lower right end of the distribution. There is a clear segregation of the locations in the BPT diagram with EW(Hα), with most of the high(low)-EW regions being located on the left(right) side of the diagram. This bimodality is a direct consequence of the LINER-like regions (less frequent) being preferentially located in the central regions of galaxies, while the SF-like regions (more frequent) are located much farther away. The above is reflected in the distributions of the average values at different galactocentric distances, being clearly above the Kewley et al. (2001) demarcation line for R < 0.6 Re (the region dominated by the bulges), but below the Kauffmann et al. (2003a) demarcation line for R > Re (the region dominated by the disk, in late-type galaxies). The most plausible explanation for this pattern is that LINER-like ionization is dominated by the contribution of hot-evolved low-mass stars (HOLMES; Flores-Fajardo et al. 2011), also known as post–asymptotic giant branch (pAGB) ionization (Binette et al. 1994). The above would be the reason why this ionization is ubiquitous in galaxies (Singh et al. 2013), and more frequently found in the central regions, dominated by old stellar populations (Belfiore et al. 2017a; Lacerda et al. 2018).
This pattern is modulated by both the stellar mass and the morphology. As galaxies are more massive and earlier, the presence of SF-like regions is less frequent. The peak of the density distribution is shifted toward the LINER-like region, which is more relevant for these kinds of galaxies. If there is still any SF activity, it is mostly located in the outer regions (at least for M⋆ > 109.5 M⊙). On the contrary, as galaxies are less massive and later, the LINER-like component becomes less relevant and the dominance of the ionization by SF processes becomes more evident (to the extent that, in the latest morphological bins (Sc/Sd), and in particular for M⋆ < 1011 M⊙, there is almost no LINER-like component in the average distribution). An additional pattern is observed in the radial trend traced by the solid gray circles in the SF-dominated regions. For galaxies more massive than M⋆ > 109.5 M⊙, there is a shift from the bottom right end of the classical location of the H ii region toward its upper left end, from the inner (0.6–0.8 Re) to the outer (1.8–2.1 Re) regions of the disks. This radial trend is directly associated with the oxygen abundance gradient observed in galaxies (e.g., Searle et al. 1973; Vila-Costas & Edmunds 1992; Sánchez et al. 2014). On the contrary, this trend is absent or even reverted for low-mass galaxies (M⋆ < 109.5 M⊙). This suggests a possible flattening of the oxygen abundance gradient in this mass bin (e.g., Belfiore et al. 2017b). These results are in agreement with those presented in Sánchez (2020), and discussed in detail in Sánchez et al. (2021), highlighting the clear connection between the ionization processes and the properties of the underlying stellar populations, which are the dominant ionizing sources in galaxies (on average). This connection is the main driver for the observed patterns and trends.
In this example, we have made use of a tiny fraction of the information distributed in the current data set. A similar analysis, using different stellar properties instead of the EW(Hα), would reveal the outlined connection. Explorations using different line ratios or other physical properties, like the stellar or gas velocity dispersion (e.g., D'Agostino et al. 2019; Law et al. 2021), are easily obtained by introducing simple modifications to the outlined procedure.
5.3. Integrated and Characteristic Parameters
From the results of the analysis performed by pyPipe3D, we derive a set of spatially resolved properties of the stellar populations and emission lines in the sample galaxies, following Sánchez et al. (2018), Sánchez (2020), and Sánchez et al. (2021). We do not distribute the entire set of resolved properties in the current release. However, we provide for each galaxy property either its integrated value (for extensive parameters, such as the stellar mass) or a characteristic value (for intensive parameters, such as the oxygen abundance), and/or a value at a certain aperture. For the integrated extensive quantities, we just coadd the corresponding values spaxel by spaxel, excluding those spaxels for which the quantity cannot be derived (we will include some examples below). For intensive properties, which present clear gradients along the galactocentric distance, we derive their azimuthally averaged radial distribution. To do so, we use the position angle, ellipticity, and effective radius (Re) provided by the NSA catalog 15 for each galaxy, to create elliptical apertures of 0.15 Re width, covering the galactocentric distance from 0 to 3.5 Re. Then, we estimate the average value for each parameter (and its standard deviation). From these radial distributions, we derive the value at the effective radius and the slope of the average gradient, based on a linear regression of the considered parameter along the radius (normalized to Re). This fitting is restricted to the galactocentric distance range between 0.5 and 2.0 Re. When the FOV does not reach this galactocentric distance, the regression is restricted to the largest distance covered by the FOV. We acknowledge that fitting the radial gradients of the explored physical properties to a linear relation is an oversimplification, in many cases, as clearly demonstrated by the results based on previous IFS-GSs (e.g., CALIFA; González Delgado et al. 2014, 2015). For a more detailed description of the radial gradients and the validity of this approximation, we refer the reader to J. K. Barrera-Ballesteros et al. (2022, in preparation). It is worth noticing that for most of the explored quantities in the galaxies, the value at the effective radius is usually considered a good proxy of the average one (e.g., Moustakas et al. 2010; Sánchez et al. 2016a), being a better representation than the mean value for data with variable FOVs (with respect to the optical extension of the galaxy).
For some parameters, we also include the central value, derived from a fixed aperture of 25 diameter (i.e., 1 FWHM of the spatial PSF). These values reveal the effects of physical processes and structures that are present in the central regions of galaxies, such as AGNs, outflows, or strong bulges, without necessarily being representative of their average properties (for a deeper discussion of this topic, see Sánchez 2020 and Sánchez et al. 2021). For some particular parameters, we also provide the values within a 1 Re aperture (i.e., not the value at the effective radius, but the value within one effective radius).
Table 18 lists all the integrated, characteristic, and aperture-limited properties derived for each cube/galaxy. For completeness, we have included additional parameters extracted from other catalogs, together with the morphological classification, photometric/structural parameters, and quality control flags described in Sections 4.3, 4.4, and 4.5. In general, the error estimated for each quantity is labeled with the same name, but with the prefix e_ (e.g., e_log_Mass corresponds to the error of the parameter log_Mass). The final catalog is included as an extension of the SDSS17Pipe3D_v3_1_1.fits FITs file distributed online. 16 This FITs file comprises three extensions. The first extension, HDU[0], is empty, just to fulfill the standard format adopted by the SDSS collaboration. The second extension, HDU[1], includes the described catalog, comprising 535 entries for each galaxy. The third extension, HDU[2], consists of row-stacked spectra, comprising the individual spectra included in the MaStars_sLOG SSP library (Section 4.2), in the format required to run pyFIT3D and pyPipe3D (Lacerda et al. 2022).
The derivation of the delivered parameters listed in Table 18 has been described in detail in many previous articles. Below, we provide a summary of the adopted procedures and some details for those parameters that require extra information.
5.3.1. Parameters Inherited from the DRP
The MaNGA DRP (Law et al. 2015) provides a single file that comprises some basic information for each of the observed targets. This information was either extracted from the NSA catalog (see Section 2) or directly from the header information. We replicate some of this information in the current catalog, to more easily identify the observed target, both in the MaNGA data set and in the sky, and to facilitate comparisons with the properties derived in our analysis. The inherited entries from the catalog comprise the first seven columns of the table, including the object name, observing plate, and IFU (ifudsgn), as already described in Section 5.1. These columns also include plateifu, a combination of the plate-ifudsgn parameters, and the unique mangaid, quantities that are frequently adopted within the community to identify MaNGA targets. The right accession and decl. of the objects, not necessarily corresponding to the center of the IFU, are also included (the objra and objdec parameters). Most parameters extracted directly from the NSA catalog are explicitly labeled with the nsa prefix (e.g., redshift, stellar mass, inclination, Sérsic index, and NSA unique identification number: nsa_redshift, nsa_mstar, nsa_inclination, nsa_sersic_n_morph, and nsa_nsaid). We also list the effective radius of the galaxy adopted in this analysis, which corresponds to the Petrosian r-band R50 parameter (Re_arc) and its position angle (PA). From these parameters, we derive the luminosity (DL) and angular (DA) distances, the effective radius in kiloparsecs (Re_kpc), the relative size of the FOV with respect to the Re (FOV, defined as the radius of the circumscribed circle around the IFU in units of effective radius, i.e., ), and the ellipticity
17
(ellip). We correct the NSA stellar mass for our cosmology (i.e., h = 0.73), but we do not apply any correction for the IMF (they adopted the Chabrier 2003 IMF).
5.3.2. Photometric and Structural Properties
In Section 4.4, we describe a set of photometric and structural properties that are included in our catalog (rows 468 to 511). For each photometric band (FILTER: u, g, r, i, B, V, and R), 18 we include: (i) the rest-frame observed magnitude (FILTER_band_mag) and its corresponding absolute magnitude (FILTER_band_abs_mag); (ii) the R50, R90, and C parameters; (iii) the B-V and B-R colors; (iv) the photometric stellar mass (log_Mass_phot); and (v) the surface brightness in the V-band at the effective radius and at R50 (V -band_SB_at_Re and V -band_SB_at_R50, respectively). For these quantities, we include the corresponding errors, estimated from an MC simulation using the error extension in the MaNGA data cubes to perturb the individual spectra. The errors are indicated with a prefix, either e_ or error_, or the suffix _error, as listed in Table 18. All the quantities were estimated from the data cubes, except M⋆,phot, which was derived from Equation (2).
5.3.3. Morphological Properties
The morphology of each galaxy was derived as described in Section 4.3. We include in the catalog all the parameters required for and derived by that procedure (rows 512–532), including: (i) the colors (u-g, g-r, r-i, and i-z) and the Sérsic index extracted from the NSA catalog; (ii) the probability P(MORPH) that the galaxy is classified in each of the 13 MORPH groups (cD, E, S0, Sa, Sab, Sb, Sbc, Sc, Scd, Sd, Sdm, Sm, and Irr); and (iii) the best morphological type estimated by the analysis, as an integer (best_type_n) and as an alphanumeric code (best_type). The parameter best_type_n runs from −2 for cD to 10 for Irr galaxies.
5.3.4. Stellar Population–related Quantities
Stellar masses. Most of the stellar parameters from which we derive the integrated and characteristic properties of each galaxy result from the decomposition of the stellar spectra in the set of SSPs described in Sections 4.1 and 5.1.1. We estimate the integrated stellar mass (M⋆; labeled as log_Mass) by coadding the stellar surface density (Σ⋆; Equation (6)) through the unmasked region (Section 5.1.7) within the FOV of the data. The regions contaminated by foreground stars included in the GAIA_MASK extension of the Pipe3D file have been masked in the derivation of any integrated quantity, including M⋆. From Σ⋆, we derive not only M⋆, but also: (i) the mass within R50 (log_Mass_corr_in_R50_V), using the parameters described in Section 4.4, and the mass within 1 Re (log_Mass_in_Re); (ii) the radius enclosing 50% of the stellar mass in kpc (R50_kpc_Mass; not to be confused with R50_kpc_V, the radius enclosing 50% of the V light in kpc, i.e., R50 transformed to kpc); (iii) the stellar-mass surface density in the central aperture (Sigma_Mass_cen), at 1 Re (Sigma_Mass_Re), and averaged over all the FOV (Sigma_Mass_ALL). The derivation of Σ⋆ is based on estimates of the mass-to-light ratio (ϒ⋆; Equation (5)), spaxel by spaxel, from which we also derive its average value across the FOV (ML_avg). A different method for estimating the average ϒ⋆ across the entire galaxy is to divide the integrated stellar mass by the integrated luminosity (ML_int). Both quantities are listed in the catalog.
Figure 11 shows the comparison between the stellar mass derived by us from our photometry (M⋆,phot; Section 5.3.2), the stellar mass provided by the NSA catalog, based on multiband photometry (M⋆,NSA; Section 5.3.1), and the stellar mass derived by spectral fitting using pyPipe3D (M⋆; Section5.3.4). We applied a systematic offset to the photometric masses. In the case of M⋆,phot, the offset is a pure consequence of the different IMF adopted (0.28 dex, according to González Delgado et al. 2016). However, in the case of M⋆,NSA, applying a similar offset produces an overcorrection, with the NSA masses being systematically larger than the masses derived by pyPipe3D. Aperture issues may be behind this difference. It is known that MaNGA apertures miss ∼22% of the total flux (e.g., Pace et al. 2019). On average, we find that an offset of 0.15 dex is a better correction for matching the NSA and pyPipe3D stellar masses. Once corrected, the photometric stellar masses agree with those derived using the stellar synthesis code within ∼0.17 dex, which is similar to the differences found by other groups (e.g., Pace et al. 2019). Furthermore, this difference is of the order of the expected error in the stellar mass due to uncertainties in the ϒ⋆–color relation, estimated to be ∼0.09–0.20 dex (e.g., Fraser-McKelvie et al. 2019; García-Benito et al. 2019).
Figure 11. Comparison between the stellar masses derived from the stellar population decomposition performed as part of the pyPipe3D analysis (M⋆) and the stellar masses derived by ourselves using only photometric information (M⋆,phot; left panel) and the stellar masses included in the NSA catalog (M⋆,NSA; right panel), both of which are included in our catalog. Offsets of 0.28 dex and 0.15 dex have been applied to M⋆,phot and M⋆,NSA, to correct for the different IMF adopted (Chabrier 2003, in both cases) and the different cosmology for the NSA values. We adopt the same format of Figure 19.
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Standard image High-resolution imageAge and metallicity. We derive from our analysis the LW and MW stellar age and metallicity, spaxel-wise, as described in Section 5.1.1, following Equations (3) and (4). We report their characteristic values at the effective radius, PAR_LW_Re_fit and PAR_MW_Re_fit, the slope of their radial gradients, alpha_PAR_LW_Re_fit and alpha_PAR_MW_Re_fit, and the corresponding errors, labeled with an e_ prefix. PAR corresponds to either the Age or the metallicity ZH. These parameters are listed in the catalog in rows 24 to 39. Additionally, we include the dust extinction at the effective radius as derived from the analysis of the stellar population, Av_ssp_Re (row 173) and its error e_Av_ssp_Re (row 174).
Star formation and chemical enrichment histories. Σ⋆, M⋆, , and
can be estimated at any lookback time (τlbt) by integrating the corresponding equations (e.g., Equation (3)) from the oldest age in the SSP decomposition down to the age matching τlbt. This is the standard procedure, as broadly applied in the literature, for deriving, for instance, the mass assembly history (MAH; Pérez et al. 2013; Ibarra-Medel et al. 2016, 2019), the SFH (Panter et al. 2007; González Delgado et al. 2017; López Fernández et al. 2018; Sánchez et al. 2019a), or the chemical enrichment history (ChEH; Vale Asari et al. 2009; Camps-Fariña et al. 2021, 2022). This derivation can be applied to integrated, characteristic, and/or spatially resolved properties. In the left panels of Figure 3, we show the normalized MAH and the ChEH at different galactocentric distances for the example galaxy/cube manga-7495-12704. Figure 12 illustrates the derivation of a spatially resolved parameter for different τlbt, by showing the cumulative Σ⋆,t
for the same prototype/example galaxy, derived by making use of the content of the SFH extension, and applying Equations (5) and (6) for a range of ages, labeled in each panel of the figure, together with the corresponding slices of the SFH cube. Integrating Σ⋆,t
through the FOV of the IFU versus τlbt, we derive the stellar mass versus time, the MAH. A correction to account for the mass of stars that are dead at the observing time, but that were still shining at τlbt, should be applied (e.g., Courteau et al. 2014). The SFH (integrated or resolved) can be obtained from the MAH (or Σ⋆,t
), by dividing the differential mass accumulated at two consecutive τ⋆,t
by the corresponding time interval, namely:
![Equation (8)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn8.gif)
where SFRssp,t (ΣSFR,ssp,t) is the star formation (density) at a particular lookback time t, Δt is the time interval between two consecutive τlbt (ages in the SSP library), and ΔM⋆,t (ΔΣ⋆,t) is the differential mass (density) assembled during Δt. When τlbt is short enough, the estimated SFR corresponds to the current one (e.g., González Delgado et al. 2016). In our catalog, we include three estimates of SFR based on the analysis of the stellar population: one corresponding to the last 32 Myr (log_SFR_ssp), adopting the time interval proposed by González Delgado et al. (2016) and already tested in Sánchez et al. (2019a), and two additional values corresponding to 10 and 100 Myr, log_SFR_ssp_10Myr and log_SFR_ssp_100Myr, respectively. The latter intervals correspond to the maximum time at which an OB star can still produce some ionizing photons before it fades out (10 Myr) and the typical time interval associated with the SFR measured using far-infrared emission (100 Myr; e.g., Kennicutt 1998; Catalán-Torrecilla et al. 2015).
Figure 12. Σ⋆ as derived from the weights included in the SFH extension, using the mass-to-light ratio of each SSP and the flux intensity in each tessella (spatial bin) for the different age ranges, indicated in the bottom left insets, corresponding to the channels in the bottom right legends. The first panel shows the actual Σ⋆, corresponding to integration along all lookback times. The subsequent panels show the cumulative Σ⋆,t
at the lookback time t, corresponding to the lower age of the considered range, without considering the redshift of the target. The age () of the stellar population and the lookback time of the universe in which it was formed (τlbt,⋆) are related via the lookback time corresponding to the redshift at which the stellar population was observed (τlbt,z
): τlbt,⋆ = τlbt,z
+
. For simplicity, in this plot, we consider τlbt,z
∼ 0.
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Standard image High-resolution imageStar formation history timescales. Following Pérez et al. (2013) and García-Benito et al. (2017), we estimate the age at which a fraction X (from 30% to 99%) of the current stellar mass was formed, based on the integrated MAH derived for each galaxy. Then, T30 (T80) indicates the time at which 30% (80%) of the stellar mass was formed. In addition, making use of the integrated and spatially resolved ChEH, we derive: (i) the average stellar metallicity, normalized to the solar value, in logarithmic scale at the same lookback times—e.g., ZH_T30 corresponds to the metallicity at the time at which 30% of the current mass was formed; (ii) the metallicity at the effective radius (ZH_Re_T%); and (iii) the radial gradient of the metallicity at the same time (a_ZH_T%). These quantities are listed in the catalog in rows 107 to 142.
Stellar spectral indices. The pyPipe3D analysis provides us with the spatial distribution of a set of stellar spectral indices for each galaxy/cube, as described in Section 5.1.3. For each index, we estimate its value at the effective radius (ID_Re_fit), the slope of its radial gradient (ID_alpha_fit), and the corresponding errors (labeled with the e_ prefix), where ID is the index identification in Table 5. The indices are listed in rows 436–467 of the final catalog (Table 18).
Spectral indices are frequently used to derive the average properties of stellar populations, such as age and metallicity (e.g., Gallazzi et al. 2005). Some indices, such as D4000, are sensitive to the age of the stellar population, tracing the fraction of young to intermediate-to-old stars, or Hδ, which is more sensitive to the presence of young stars. Other indices, like Mgb, are sensitive to metallicity. Since indices permit an alternative method of analysis to the stellar decomposition at the core of pyPipe3D, it is worth comparing the results from both methods. Figure 13 shows: (i) the distribution of Hδ as a function of D4000; (ii) the distribution of as a function of these two indices; and (iii) the distribution of
as a function of Mgb, where all the index values are estimated at the effective radius. As additional information, we indicate the locations of the different morphological types in these diagrams. The Hδ–D4000 diagram shows the typical distribution observed for the bulk population of nearby galaxies (e.g., Kauffmann et al. 2003b), a clear anticorrelation, in the sense that galaxies with higher Hδ (young stellar populations) have a lower D4000 (a lower fraction of old stars with respect to young ones). Late-type (early-type) galaxies are found in the upper left (lower right) area of the distribution.
shows a well-defined positive (negative) relation with D4000 (Hδ), with the different morphological types following the expected behavior. Finally,
presents a well-defined positive relation with Mgb, which seems to be less tight than that tracing
with D4000 or Hδ. As expected, early-type (late-type) galaxies are found in the regime of high (low)
and Mgb values. This comparison suggests that our estimated stellar indices are indeed good tracers of the stellar content.
Figure 13. Top left panel: distribution of the Hδ vs. D4000 spectral index, measured at the effective radius for the full sample of galaxies (with contours encircling 95%, 65%, and 40% of the objects, successively). The locations of the different morphological types (E, S0, Sa/b, and Sc/d) are color coded in the shaded areas. Histograms of the parameters segregated by morphological type are presented at the top and right of each panel. Top right panel: distribution of vs. D4000. Bottom left panel: distribution of
vs. Hδ. Bottom right panel: distribution of
vs. Mgb.
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Standard image High-resolution image5.3.5. Emission-line-related Quantities
We derive a wide variety of parameters based on the emission-line analysis performed by pyPipe3D. First, we determine which are the most frequently detected emission lines. To do so, we explore how often the different emission lines included in the FLUX_ELINES_LONG extension (Section 5.1.5, Table 14) are detected in all the analyzed data cubes. We select emission lines that are detected with an S/N larger than 3 in at least 5% of all galaxies/cubes (i.e., in at least ∼500 galaxies). For these emission lines, we estimate the flux intensity at the effective radius and the slope of the radial gradient (flux_ELINE_Re_fit and flux_ELINE_alpha_fit), as well as their corresponding errors (labeled with the e_ prefix), following the procedure described above (Section 5.3). For each parameter, ELINE corresponds to the Id+wavelength listed in Table 14, where the most frequently detected emission lines are labeled with an "*" symbol. All these parameters and errors comprise the rows 180 to 307 of the final catalog, as listed in Table 18.
Line ratios. For those emission lines that are more frequently used in the classical diagnostic diagrams (i.e., [O ii]λ3727, [O iii]λ5007, [O i]λ6300, [N ii]λ6584, and [S ii]λ6717,31), we derive their ratios with respect to Hβ (in the case of [O ii] and [O iii]) and Hα (for the remaining lines), using the values included in the FLUX_ELINES extension at: (i) the central aperture (_cen suffix); (ii) the effective radius (_Re suffix); and (iii) the average across the full FOV of the data cube (_ALL suffix). In addition, we estimate the equivalent width of Hα (EW_Ha_), and the Hα-to-Hβ ratio (Ha_Hb) at the three locations. The corresponding errors have been estimated, too. Rows 13 to 21 and 70 to 100 comprise all these parameters. A simple nomenclature, including the two line ratios involved, is adopted to label these quantities (e.g., log_OI_Ha_cen corresponds to the line ratio between [O i] and Hα in the central aperture in logarithmic scale, while including an e_ prefix means that it corresponds to its error). The Hα flux in the central aperture is also included for the purpose of comparison with the SDSS single-aperture fiber data (rows 162–163). A practical example of the use of these line ratios is included in Section 7.
Dust extinction. We use the Hα-to-Hβ ratio to derive the dust extinction of the ionized gas (AV,gas), by assuming a nominal ratio of 2.86. This value corresponds to a nebula fulfilling the case-B recombination scenario, with an electron density of ne = 100 cm−3 and an electron temperature of Te = 104 K (Osterbrock 1989). We adopted an MW-like dust extinction (Cardelli et al. 1989), with a total-to-selective extinction RV = 3.1. By conducting this derivation spaxel by spaxel, we are able to derive a dust extinction map for each galaxy/cube (AV ). From those maps, we estimate the dust extinction at the effective radius (Av_gas_Re) and its error, included in the final catalog in rows 171–172. These AV maps are used to correct all the observed emission lines by their dust extinction, providing dust-corrected emission lines that can be used in the derivation of additional parameters.
Oxygen and nitrogen abundances. The emission-line maps can be used to examine the possible ionizing sources spaxel by spaxel. In particular, following the criteria described in detail in Sánchez (2020) and Sánchez et al. (2021), we classify as SF areas those regions below the Kewley et al. (2001) demarcation line in the classical BPT diagram involving [O iii]/Hβ and [N ii]/Hα with EW(Hα) > 3Å. For those regions, and only for them, it is possible to estimate the oxygen and nitrogen abundances, and the ionization parameter, based on strong line indicators (since those are only calibrated for ionization due to young OB stars, associated with recent SF activity). The dust-corrected emission-line maps are used for the derivation of the oxygen abundances using the different calibrators adopted along this exploration. For the central aperture, we estimate the oxygen abundance using the calibrators adopted in Sánchez et al. (2019b), which comprise a mixed set, including calibrators anchored to measurements based on the direct method and calibrators derived using photoionization models. Among them, we include here the O3N2- and N2-based calibrators proposed by Marino et al. (2013; OH_O3N2_cen and OH_N2_cen), the ONS calibrator by Pilyugin et al. (2010; OH_ONS_cen), the R23-based calibrator by Kobulnicky & Kewley (2004; OH_R23_cen), the pyqz calibrator of Vogt et al. (2015; OH_pyqz_cen), the calibrators of Maiolino et al. (2008; OH_M08_cen), Tremonti et al. (2004; OH_T04_cen), Dopita & Sutherland (1996; OH_dop_cen), and Pérez-Montero & Contini (2009; OH_O3N2_EPM09_cen), and the t2-corrected calibrator of Peña-Guerrero et al. (2012), as derived by Sánchez et al. (2019b; OH_t2_cen). All these oxygen abundances and their errors are included in rows 50 to 69 of the final catalog.
A larger and more complete set of calibrators was recently used by Espinosa-Ponce et al. (2022) in their exploration of the properties of H ii regions. We adopt most of those calibrators to derive the oxygen and nitrogen abundances (or the nitrogen-to-oxygen abundance ratio), together with the ionization parameter, for those spaxels whose ionization is compatible with young massive OB stars (i.e., associated with recent SF). The complete list of adopted calibrators is included in Table 15 of Section D. Once derived, those parameters spatially resolved (i.e., spaxel by spaxel), and we estimate their corresponding values at the effect radius (OH_CAL_Re_fit, where CAL corresponds to the ID of each calibrator), the slope of their radial gradient (OH_CAL_alpha_fit), and their errors (labeled with the prefix e_). All these values are included in the final catalog, covering rows 308 to 431. They comprise a total of 24 oxygen abundance estimates, four different nitrogen (and nitrogen-to-oxygen) estimates, and four estimates of the ionization parameter.
Figure 14 shows a comparison of a subset of the oxygen abundance calibrators included in the final catalog as a function of the values derived using the Ho (2019) calibrator, selected as an arbitrary fiducial oxygen abundance calibrator following Espinosa-Ponce et al. (2022). This calibrator implements a state-of-the-art machine-learning routine to provide a calibrator that anchors the oxygen abundance to direct measurements using the direct method. It uses a large number of emission-line ratios, as listed in Table 15, providing a quite accurate estimation of the oxygen abundance. A similar comparison for the full set of calibrators is included in Appendix D. It is beyond the scope of the current study to discuss in detail the well-known discrepancies among different O/H calibrators and the possible transformations between them (e.g., Kewley & Ellison 2008; López-Sánchez et al. 2012; Curti et al. 2020; Espinosa-Ponce et al. 2022). The main aim of this comparison is to show that there is indeed a correspondence between the oxygen abundances derived using different calibrators included in our catalog. This correspondence is far from being a one-to-one relation in most of the cases. In a few cases, the oxygen abundances present just a constant offset for all the considered dynamical range (e.g., the Kew02 N2O2 and Pil16 R and S calibrators). For a large number of calibrators, the relation is well characterized by an almost linear relation, with a slope different than 1 (e.g., Cur20 N2, Mar N2, Pet04 N2). In some of them, the dynamical range is considerably different from calibrator to calibrator (e.g., Cur20 R2 or RS32), which would translate into a large variety of slopes for the linear relation that would match them. Finally, there are cases in which there are clear deviations from the linear relation, with a change in the slope or even a plateau found at high abundances (e.g., Pil10 ON and NS), or even large discrepancies in the same regime (e.g., T04). In summary, any analysis using the oxygen abundances included in the delivered catalog should acknowledge the described differences among the different calibrators, and the impacts on the results. For instance, explorations of the shape of the mass–metallicity relation (MZR) or the oxygen abundance gradient are deeply affected by the adopted calibrator, both qualitatively (e.g., the presence of plateaus in the distribution of the MZR; Sánchez et al. 2019b; Alvarez-Hurtado et al. 2022) and quantitatively (e.g., the actual values of the radial gradients; Belfiore et al. 2017b; Sánchez-Menguiano et al. 2018).
Figure 14. Comparison between the oxygen abundances, 12+log(O/H), derived at the effective radius using four different calibrators (one for each panel) and listed in the final catalog as a function of the values derived using the Ho (2019) one (adopted as the fiducial one in an arbitrary way). We adopt the same format as described in Figure 19 for the different panels of the figure.
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Standard image High-resolution imageA less relevant difference is found among the three estimates of the nitrogen-to-oxygen relative abundance included in our catalog, as seen in Figure 15. They present clear linear relations among them, with a slope near to 1, and small offsets (Δlog(N/O) ∼ 0.03–0.08 dex) and scatters ( 0.03–0.07 dex). In this case, the use of a different calibrator would produce little quantitative difference and essentially no qualitative differences.
Figure 15. Comparison between the three values for N/O relative abundance measured at the effective radius included in the final catalog. We adopt the same format as described in Figure 19. Details of the compared quantities are given in Section 5.3.
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Standard image High-resolution imageStar formation rate. The dust-corrected Hα luminosity is used to estimate the SFR spaxel-wise, by applying the relation proposed by Kennicutt (1998) for the Salpeter (1955) IMF:
![Equation (9)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn9.gif)
From this distribution, it is possible to derive the SFR surface density (ΣSFR). As in the case of the stellar mass, we can derive the integrated SFR just by coadding the spaxel-by-spaxel values across the FOV of the IFU data. As already discussed in previous articles (Cano-Díaz et al. 2016; Sánchez et al. 2018), this SFR is an upper limit to the real one, since in this derivation all Hα flux is integrated, irrespective of the nature of the detected ionization. In other words, this calculation would derive an SFR even for those galaxies in which there is no ionization that could be directly associated with a recent SF event (i.e., in the case of RGs, such as elliptical ones). However, it is still useful as an upper limit, in the event that a fraction of this ionization is still due to SF, but it is so weak that it is overshadowed by ionization due to other sources (i.e., shocks, ionization by old stars, like pAGBs and HOLMESs, or AGNs; see Sánchez et al. 2021, and references therein). We include this integrated SFR in the catalog (log_SFR_Ha), together with its error (e_log_SFR_Ha), in rows 7 and 11. However, for completeness, we also derive the SFR just by coadding those regions in each galaxy where ionization is compatible with recent SF, using the combination of the BPT diagram plus the EW(Hα), as described above (log_SFR_SF). Furthermore, we estimate the SFR after considering the possible contamination from a diffuse ionization gas, due to old stars (e.g Binette et al. 1994; Flores-Fajardo et al. 2011), assuming a constant EW(Hα) = 1 Å for this component (log_SFR_D_C).
Figure 16 shows the comparison between the SFR derived using the dust-corrected Hα luminosity (SFRHα ) and the values derived based on the stellar synthesis analysis (SFRssp) for three different timescales (10, 32, and 100 Myr), and for the average of the three of them. We find a clear correspondence between the four SFRssp with SFRHα , with the average of the three timescales providing the lower offset with respect to the one-to-one relation (Δlog(SFR) = 0.06 ± 0.30 dex), followed by the value for 32 Myr. On average, the SFR derived for 10 Myr (100 Myr) is higher (lower) than the one estimated using the Hα luminosity. These results agree with previous explorations using different IFS data sets (e.g., González Delgado et al. 2016; Barrera-Ballesteros et al. 2021a). In summary, the use of SFRssp instead of SFRHα would provide similar statistical results—for instance, when exploring global relations such as the SF main sequence (e.g., Sánchez et al. 2018, 2019a). In principle, this quantity should be more accurate, since it does not involve any assumed SFH or ChEH, as it is necessary to derive the scaling between the Hα luminosity and the SFR (Kennicutt 1998). However, it is most probably less precise, due to the additional uncertainties introduced by the stellar synthesis analysis in the derivation of this quantity. As a consequence, the derived relations may present a larger scatter.
Figure 16. Comparison between the derivation of the SFR based on the dust-corrected Hα luminosity (SFRHα ) and four different estimates based on the stellar population decomposition, performed as part of the pyPipe3D analysis (SFRssp), for three time ranges: 10 Myr (top left panel), 32 Myr (top right panel), and 100 Myr (bottom left panel), as well as the average of the three (bottom right panel). We exclude those galaxies without evidence of SF activity from this comparison by excluding those ones for which the ∣EW(Hα)∣ < 3 Å at the effective radius. We adopt the same format as described in Figure 19. Details of the compared quantities are given in Section 5.3.
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Standard image High-resolution imageMolecular gas estimation. The dust extinction is a tracer of the molecular gas content, via the dust-to-gas relation (e.g., Brinchmann et al. 2004, and references therein). We adopt the recent calibrator from Barrera-Ballesteros et al. (2021b) to estimate the molecular gas surface density (Σmol) through the spaxel-by-spaxel AV,gas parameter, using the formula
![Equation (10)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn10.gif)
then, by coadding throughout the FOV of each IFU, we estimate the integrated molecular gas (log_Mass_gas_Av_gas_log_log, row 170). For completeness, we provide the same parameter estimated using the linear calibrator proposed by Barrera-Ballesteros et al. (2020):
![Equation (11)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn11.gif)
which is included in row 40 (log_Mass_gas). We do not provide the formal errors of these parameters, since the error budget is dominated by the calibrators themselves (∼0.2 dex), rather than by the uncertainties in the derivation of the dust extinction.
Two additional estimates of the integrated molecular gas mass have been included in the catalog. One of them is derived by applying a possible correction that takes into account the dependence of the dust-to-gas ratio on the oxygen abundance (log_Mass_gas_Av_gas_OH), and the other is derived by adopting this correction, but by using the stellar dust extinction instead of the ionized gas one in the estimation of the molecular gas surface density (log_Mass_gas_Av_ssp_OH). To derive these estimates, we followed the main procedures described in Barrera-Ballesteros et al. (2021b). However, the dynamical range of the metallicity explored in that study was too narrow to provide a clear conclusion regarding the improvements as a result of introducing these two new calibrators. We include them here in order to allow future explorations to compare them with other estimates of the molecular gas.
Electron density. Finally, we use the [S ii]λ6717,31 line ratio to derive the electron density spaxel by spaxel, solving the equation
![Equation (12)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn12.gif)
where x = 10−4 ne t−1/2, t is the electron temperature in units of 104 K (McCall et al. 1985), and ne is the electron density in units of cm−3. As in the case of the dust extinction, we adopted a typical electron temperature of Te = 104 K in this derivation. We note that the dependence of the electron on this parameter is weak in the adopted formula. As in the cases of other parameters, we deliver the value at the effective radius (Ne_Oster_S_Re_fit), the slope of its radial gradient (Ne_Oster_S_alpha_fit), and their corresponding errors for each galaxy/cube (rows 432–435).
5.3.6. Kinematics-related Quantities
The analysis performed on the data provides the spatial distribution of the stellar and ionized gas velocity and velocity dispersion. From these quantities, we derive different characteristic parameters for each galaxy/cube, which are included in the final catalog: (i) the average velocity-to-velocity dispersion ratio within an aperture of 1 Re (; labeled as vel_sigma_Re, row 45), and its corresponding error (e_vel_sigma_Re, row 46), estimated as
![Equation (13)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn13.gif)
where f⋆, v⋆, and σ⋆ correspond to the stellar flux intensity in the V band at any position (x,y) within the FOV, the stellar velocity, and the stellar velocity dispersion in each spaxel within the considered apertures; (ii) the stellar and Hα ionized gas velocities (vel_ssp_R and vel_Ha_R), and their corresponding errors (labeled with an e_ prefix), derived in an elliptical ring (following the PA and ellipticity of the galaxy) at 1 and 2 Re (R = 1 or 2), listed in rows 145 to 152; (iii) the stellar and Hα ionized gas velocity dispersion (vel_disp_ssp_R and vel_disp_Ha_R) in the central aperture (R = cen) and at 1 Re (R = 1Re), listed in rows 155 to 158; and (iv) the apparent stellar angular momentum parameter at the effective radius (Emsellem et al. 2007), (labeled as Lambda_Re, row 175), and its corresponding error (e_Lambda_Re, row 176), defined as
![Equation (14)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn14.gif)
where f⋆, v⋆, and σ⋆ correspond to the same parameters as in Equation (13), and r is the deprojected galactocentric distance.
For the current data set, we introduce an inclination correction on λ that was not included in the calculations for DR14 and DR15. This correction is described in the Appendix of Emsellem et al. (2011):
![Equation (15)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn15.gif)
where λ corresponds to the apparent angular momentum described in Equation (14) and i is the inclination angle of the galaxy.
5.3.7. Volume Correction
The MaNGA sample has a complicated selection function. As already outlined in Section 2, the final sample was built from a set of different subsamples, each of them adopting different selection criteria. Despite this complicated construction, in principle, it is possible to perform a volume correction and derive representative quantities from this sample. Wake et al. (2017) proposed a volume correction in which the volume accessible for each subsample is explored separately, and then the full volume accessible for each individual object is evaluated. In Sánchez et al. (2019a), Appendix E, we proposed a different approach, in which the individual volume accesible for each target in the final sample is estimated a posteriori. The procedure is described in detail in Rodríguez-Puebla et al. (2020). In summary, we adopted the stellar-mass function (Blanton et al. 2017) of the SDSS galaxies to estimate the volume accessible in a set of bins of stellar mass, redshift, and color for this parent sample (as all MaNGA galaxies are extracted from the SDSS spectroscopic sample). Then, we estimate the fraction of MaNGA galaxies in each of those bins with respect to the total number of SDSS galaxies. Considering this fraction and the previously estimated volume, it is possible to estimate, for each galaxy, its accessible volume. We repeat this calculation for the final MaNGA sample, deriving, for each galaxy, the weight to correct for its accessible volume (Vmax_w) and number (Num_w), which are included in the final catalog as rows 533 and 534, respectively. Figure 17 shows the comparison between the original stellar-mass function adopted to estimate the accessible volume for each galaxy (i.e., the SDSS one) and the one derived from our data using these estimated volume corrections. There is considerable agreement between both distributions in the regime at which the sample may be considered representative and statistically significant, i.e., for M⋆ > 109 M⊙. Below this stellar mass, the current sample is clearly incomplete.
Figure 17. Stellar-mass function derived using the the volume corrections included in our final catalog (solid white circles) compared with the one derived using a volume-complete sample extracted from the NSA catalog (Blanton et al. 2017), shifted in mass, to correct for the different adopted IMFs, and in density, to match our adopted cosmology (i.e., h = 0.73). The agreement is particularly good for the mass range above 109 M⊙.
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Standard image High-resolution image6. Comparison with Previous Results
The current (and previous) versions of the MaNGA data sets have been extensively analyzed with earlier versions of our pipeline and with different tools and procedures developed within the community. The results of these analyses are publicly accessible, in many cases. Here, we present a brief comparison with published data sets in order to determine which scientific results may change (or not) when using different analyses, anchoring our results to the published ones when possible.
6.1. Morphological Classification
Different groups have addressed the morphological classification of the MaNGA galaxies. Among them, we highlight the visual classification presented by Vazquez-Mata et al. (2022) as a MaNGA VAC, 19 which has been the basic training data set for our own classification. Domínguez Sánchez et al. (2022; hereafter, DS22) present a detailed state-of-the-art morphological classification based on supervised deep-learning models applied directly to SDSS images. 20 They use two different visual classification catalogs to train their method (Nair & Abraham 2010; Willett et al. 2013). This analysis provides a T-type for each galaxy, together with the automatic identification of edge-on and barred galaxies. Our morphological classification is expected to provide a statistical segregation of the different morphological types, matching the visual classifications mentioned above. In Figure 18, we compare the results from both methods as a violin plot of the DS22 T-types versus our morphological classification (the best_type_n parameter). Despite the significant differences between both methods, the agreement between both morphological classifications for most types is remarkable. The largest differences are found at the extremes (best_type_n < 1 and best_type_n > 8). On the one hand, mild but clear differences are found in the merging spiral (Sm) and irregular (Irr) galaxy types. This is expected, since both schemes rely on different visual classifications of the galaxies. The more regular the galaxy, the better the expected agreement between both methods, as is apparent in Figure 18. On the other hand, the E and S0 galaxies in our catalog present very similar T-type distributions as in the DS22 catalog. We stress that DS22 consider that their T-type number is not enough to distinguish between these two morphological types, proposing a more complex decision tree based on additional parameters. 21 Our comparison indeed supports their results in this regard.
Figure 18. Comparison between the morphological classification included in our final catalog (Section 4.3) and the values reported by DS22. The violin plots show the distribution of the DS22 T-type parameter for each of our morphological classes. The upper panel shows a histogram of the number of galaxies in each morphological bin in a logarithmic scale.
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Standard image High-resolution imageThe largest differences are found in the cD-class, which is included in our classification scheme as best_type_n = − 2, but absent from DS22. Based on this result, we consider that our classification for these galaxies is dubious. In some cases, such as manga-11968-6103, it may indeed be a cD galaxy from visual inspection. However, there are other galaxies, such as manga-8144-3703, which we consider should be classified as Irr (it was classified as Scd/Sd by DS22). In summary, the cD morphological type should be excluded from any further analysis. In any case, its removal has a negligible impact from a statistical point of view, since it only comprises eight galaxies. For the remaining galaxies, our morphological classification is at least as good as the published ones for statistical analysis.
6.2. Values Reported by Pipe3D in Previous DRs
As already mentioned, we have published previous versions of the analysis presented here, using an outdated version of the Pipe3D pipeline and a different set of SSP templates for the stellar decomposition analysis. In Sánchez et al. (2016a), we presented this analysis for the IFS data of the CALIFA DR2 data set (García-Benito et al. 2015). This distribution is a very limited catalog, with just 12 stellar and ionized gas characteristic/integrated properties, using a previous version of the Pipe3D data products for each of the 200 analyzed cubes (including only the SSP, SFH, FLUX_ELINES, and INDICES extensions). A similar analysis was presented in Sánchez et al. (2018) for the MaNGA DR14 data set for ∼2800 galaxies. In that case, we distributed a catalog 22 comprising almost 100 characteristic/integrated properties, in addition to the corresponding set of Pipe3D files (one per galaxy). Finally, we distributed exactly the same set of properties and data products for the MaNGA DR15 data set, 23 including ∼4500 galaxies.
Here, we present a comparison between a set of selected properties derived for DR17 (the current analysis) with their DR15 counterparts, for the objects in common. Figure 19 shows this comparison for frequently used stellar parameters, including: (i) integrated stellar mass (M⋆); (ii) average mass-to-light ratio (ϒ⋆); (iii) SFR derived from the analysis of the stellar population (SFRssp); (iv) LW age at the effective radius (); (v) LW metallicity at the effective radius (
); (vi) average stellar dust extinction (A⋆,V
); (vii) stellar velocity dispersion in the central aperture (σ⋆,cen); (viii) stellar velocity at the effective radius (
); and (ix) apparent angular momentum (
).
Figure 19. Comparison between a set of stellar properties derived using Pipe3D for the ∼4500 galaxies in MaNGA DR15 and using pyPipe3D for the same galaxies in MaNGA DR17. We show only the galaxies/cubes reported in both analyses with good quality. Each panel shows a density distribution in the DR15 vs. DR17 parameter diagram as a set of filled contours, with each consecutive contour encircling 99%, 95%, and 65% of the points (i.e., 3σ, 2σ, and 1σ), respectively. The dashed line shows the one-to-one relation. The upper left inset shows the density distribution of the difference between the DR15 and DR17 values for the corresponding parameter. The mean value of this difference (Δ) and its standard deviation are shown in the lower right legends of each panel. From left to right, and from top to bottom, we show this comparison for: stellar mass (M⋆), average mass-to-light ratio (ϒ⋆), SFR derived from the analysis of the stellar population (SFRssp), LW age at the effective radius (), LW metallicity at the effective radius (
), average stellar dust extinction (A⋆,V
), stellar velocity dispersion in the central aperture (σ⋆,cen), stellar velocity at the effective radius (
), and apparent angular momentum (
). For details of the derivations of these quantities, see Section 5.3.
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Standard image High-resolution imageThe stellar mass, shown in the upper left panel, presents a small systematic offset of ∼−0.07 dex between the two distributions, with the DR15 masses being slightly larger than the DR17 ones. Considering that the two data sets have been reduced using different versions of the MaNGA DRP (v2_4_3 versus v3_1_1), and that we use a completely different SSP library, some systematic differences are expected. This difference is considerably smaller than the scatter between both values, characterized by a standard deviation σM⋆ ∼ 0.21 dex. Most of this scatter cannot be attributed to photometric differences, as the expected spectrophotometric precision and accuracy of the MaNGA data is of the order of ∼5% (Yan et al. 2016a, 2016b). Indeed, a simple comparison of the V-band photometry measured directly from the DR15 and DR17 data cubes yields a difference of ∼0.02 ± 0.05 dex. We consider that the scatter observed in M⋆ is introduced by differences in the estimated ϒ⋆, which are a combination of the use of a different stellar population template and, to a lesser extent, differences in the fitting procedure. This is evident from the comparison of the results for ϒ⋆, shown in the upper middle panel of Figure 19. Although there is a clear correspondence between the two values, near a one-to-one relation for ϒ⋆ < 7.5 M⊙/L⊙, there is a deviation, with the DR15 values being slightly lower than the DR17 values for ϒ⋆ > 7.5 M⊙/L⊙. This introduces a bias of −0.26 M⊙
L
in Δϒ⋆. The considerable scatter in the distribution, with σϒ⋆ = 1.41 M⊙
L
in Δϒ⋆, corresponding to ∼0.15 dex on average, is clearly the main driver for the observed differences in the stellar masses. This scatter is propagated to any other quantity that requires the use of ϒ⋆, like the SFR derived from the stellar population spectral decomposition (Section 5.3.4), shown in the upper right panel of Figure 19. In this case, we compare the SFRssp estimated at 32 Myr. There is clear agreement between the DR17 and DR15 SFR values, following on average a one-to-one relation. This is reflected in the small average difference (∼−0.03 dex). However, the dispersion around the one-to-one relation, ∼0.37 dex, is larger than the dispersions reported for both ϒ⋆ and M⋆. This is expected, since the derivation of SFR depends directly on the derivation of the two previous parameters, with the additional uncertainty of estimating the stellar mass for just a tiny fraction of the total stellar component (younger than 32 Myr, according to Equation (8)).
The center left panel of Figure 19 shows the comparison of the LW stellar age. Again, there is a correspondence between the DR15 and DR17 values. However, the relation is not just on top of the one-to-one line. There is a clear systematic offset of ∼0.1 dex, with the ages derived for DR15 being older than those for DR17. The difference is small compared to the dynamical range of the parameter (∼2 dex), but it is well determined, even considering the scatter around the central relation, = 0.14 dex. We built the age sampling of the new SSP template following a philosophy similar to the one that was adopted for generating the GSD156 template (a pseudologarithmic sampling of the time steps). However, neither the detailed set of ages nor the range of metallicities (and their sampling) are equal between the two sets of SSPs. In addition, the two templates use different stellar libraries (MILES versus MaStar), different sets of isochrones, and were computed by two different synthesis codes. All together, this can easily explain the observed age differences, which, in any case, are rather small.
Larger differences are found for the LW stellar metallicity (the center middle panel of Figure 19). For this parameter, we find a one-to-one correspondence between the two data sets for the stellar
,DR17 > −0.5 dex. Below this value, the distribution bends toward a plateau for the DR15 metallicities at ∼−0.3 dex. Despite this deviation from the one-to-one relation, the average difference between the two values is rather small, with a Δ
= 0.06 ± 0.18 dex. The reported differences are most probably due to the different sampling and coverage of stellar metallicity in the newly adopted SSP template, ranging from
= −2.30 to 0.30 dex, sampled for seven metallicities, compared to the DR15 template, ranging from −0.73 to 0.20 dex, sampled for four metallicities. However, this alone cannot explain the observed difference, since the plateau is not reached at the minimum sampled metallicity for the GSD156. A combination of the difference in the metallicity coverage, the different stellar libraries, the adopted isochrones, and the different synthesis codes must be behind the observed behavior. As a result of this difference, similar qualitative results will be found when using the DR15 and DR17
values, but the quantitative results will change. Global relations, like the stellar MZR, or spatially resolved trends, like radial gradients, will present a wider dynamical range for the DR17 data set. However, we do not expect strong changes, either in the shapes of the relations, or in the signs of the gradients.
The right middle panel of Figure 19 shows the comparison between the dust extinction for the two data sets. As in the case of the two previous parameters, there is a well-defined correspondence between the two values. The best agreement is found for low dust extinction values (AV < 0.3 mag), with a distribution near the one-to-one relation. For larger values, the AV reported in DR15 is slightly higher than for DR17, which translates into a positive ΔAV = 0.07 ± 0.17 mag. Again, despite the lack of a one-to-one correspondence, for the three cases shown in the central panels these differences are rather small, being just two or three times larger than the expected errors for the parameters (Figure 6 of Lacerda et al. 2022).
In the lower panels of Figure 19, we compare the DR15 and DR17 results for the kinematical parameters: σ⋆,cen, vel, and
. For σ⋆,cen and vel
, the distributions follow a one-to-one relation, with offsets lower than the reported standard deviations: Δσ⋆,cen = −12 ± 37 km s−1 and Δvel⋆,cen = −17 ± 61 km s−1. In the case of σ⋆,cen, the scatter of the difference is of the order of the expected errors for this parameter, based on simulations, ∼22 km s−1 (Lacerda et al. 2022), and slightly smaller than the instrumental resolution, ∼75 km s−1 (e.g., Law et al. 2021). For the stellar velocity at the effective radius, the scatter of the difference is three times larger than the expected errors, ∼20 km s−1 (Lacerda et al. 2022), being of the order of the instrumental resolution. In both cases, the differences are larger for the lowest values of the parameters, in particular for the velocity. In general, we do not anticipate any qualitative or quantitative differences in the results derived from both data sets relating to these two kinematic parameters. Larger differences between the DR15 and DR17 values are found for
, due to the inclination correction described in Section 5.3.6 (Equation (15)). Once corrected,
follows the DR17 results along the one-to-one relation (the lower right panel of Figure 19). The two values agree within
= 0.4 ± 0.19, with the larger discrepancies being found for the larger values of the angular momentum.
Figure 20 shows the same type of comparison for a selected subset of the parameters derived from the ionized gas emission lines included in the final catalog (Section 5.3.5). We compare: (i) the Hα flux (FHα,cen); (ii) the equivalent width of Hα (EWHα,cen); (iii) the Hα-to-Hβ line ratio; (iv) the [O iii]-to-Hα line ratio; (v) the [N ii]-to-Hα line ratio; and (vi) the [S ii]-to-Hα line ratio, all measured in the central aperture. Additionally, we include: (vii) the average dust extinction across the entire galaxy derived from the Hα-to-Hβ line ratio; (viii) the integrated SFR derived from the Hα luminosity; and (ix) the molecular gas integrated mass derived from the dust-to-gas ratio. In most cases, we find quite good agreement between the two estimates of the parameter. In the case of FHα,cen, there is a tight one-to-one relation, with an offset lower than the standard deviation over most of the dynamical range. Most of the scatter and the deviation from the one-to-one relation is found at very low flux intensities, where the differences are driven by the accuracy of the subtraction of the underlying stellar population, rather than by the properties of the emission lines themselves (e.g., their S/Ns). Indeed, the scatter presents a standard deviation of ∼0.34 dex, three times larger than expected, based on the emission-line properties (∼0.1 dex; Figure 9 of Lacerda et al. 2022). For log(FHα,cen,DR17) < −1.5 dex, the distribution bends, with the DR15 values reaching a plateau. The newly adopted SSP template is based on a stellar library observed with the same instrument and reduced with the same tools, and therefore its spectral resolution should better match that of the observed data. Therefore, we consider that the new derivation of the emission-line fluxes is more reliable, in particular in the central regions, where the flux intensity of the emission lines is low, in general, and the stellar component is brighter and older (i.e., the worst-case scenario for a proper estimate of the emission-line properties). For the same reasons, a similar trend is observed for the EW(Hα), although in this case the agreement with respect to the one-to-one relation seems to be slightly better, with a Δlog (EW(Hα)) = 0.07 ± 0.19.
Figure 20. Comparison between a set of ionized gas properties derived using Pipe3D for the ∼4500 galaxies in MaNGA DR15 and using pyPipe3D for the same galaxies in MaNGA DR17. We show only the galaxies/cubes reported in both analyses with good quality, using the same format adopted in Figure 19. From left to right, and from top to bottom, we compare: Hα flux (FHα,cen), equivalent width of Hα (EWHα,cen), Hα-to-Hβ line ratio, [O iii]-to-Hα line ratio, [N ii]-to-Hα line ratio, and [S ii]-to-Hα line ratio, all of them at the central aperture; and the average dust extinction across the optical extension of the galaxy derived from the Hα-to-Hβ ratio, the integrated SFR derived from the Hα luminosity, and the molecular gas integrated mass derived from the dust-to-gas ratio. For details of the derivations of these quantities, see Section 5.3.
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Standard image High-resolution imageWe find similar results for the four line ratios in Figure 20. In all cases, the bulk of the distribution is located around the one-to-one relation, with tiny offsets of ∼0.0–0.5 dex and a scatter of ∼0.13 dex. The largest differences are found for the Hα/Hβ ratio, which shows a tail toward larger values for DR17 compared to DR15 below <2.8. This tail is not observed in the other line ratios, indicating that it is most probably related to an effect that affects the Balmer lines simultaneously. We consider that the differences in the SSP templates are behind this effect. In general, based on these results, none of the conclusions regarding the ionization conditions, and most probably none of the properties derived from the line ratios (e.g., the oxygen abundance and the ionization parameter), should be affected by the choice of either the DR15 or the DR17 data products.
The lower row of Figure 20 shows the comparison for a set of physical properties derived from the emission-line parameters. The lower left panel shows the comparison of the dust extinction at the effective radius, where we find a systematic offset of ΔAgas,V = 0.24 ± 0.27 mag. We do not have a clear explanation for the origin of this difference, since, in principle, all the analysis was repeated following exactly the same steps. We can only guess that the masking associated with the minimum S/N required to compute the radial distribution of the dust extinction has introduced this change. In any case, this offset has not introduced any significant changes in the integrated properties included in the two final panels of this figure. The lower middle panel of Figure 20 shows the comparison between the integrated SFR, derived using the dust-corrected Hα luminosity. As for the rest of the properties, the distribution lies around the one-to-one relation, in particular for SFRHα,DR17 > 10−2 M⊙ yr−1, i.e., for most SFGs. At low SFRs, the DR15 values are slightly larger, following a similar trend (and most probably for the same reasons) as the distributions for FHα,cen and EW(Hα). Finally, the lower right panel of Figure 20 shows the comparison between the molecular gas mass estimated from the dust extinction. In this particular case, we use the log_Mass_gas parameter listed in the catalog (Section 5.3.5). The distribution is centered on the one-to-one relation. However, this parameter shows a larger scatter than the previous one (∼0.48 dex), reflecting a considerable variation in the spaxel-by-spaxel dust extinction between the two DRs. Since this parameter is derived from the Hα/Hβ ratio, from the discussion regarding the top right panel of the figure, most likely this scatter is due to differences in the subtraction of the underlying stellar population introduced by the new SSP library (and its effects on the estimates of the shape and depth of the Hα and Hβ stellar absorption).
6.3. Values Reported by the MaNGA Data Analysis Pipeline
As already mentioned, the MaNGA DR17 data have been analyzed using other tools. In particular, all the data set was processed using the MaNGA DAP (Westfall et al. 2019). 24 This tool performs a decomposition of the stellar and ionized gas components of the observed spectra, using different spatial binning schemes, delivering the spatial distribution for a set of properties of the emission lines, a set of stellar indices, and the stellar kinematic properties. From these data products, a set of characteristic values for each galaxy estimated at the central regions (for the emission lines) and at the effective radius (for the stellar indices) are extracted, which are then integrated into the DAPall database. In this database, parameters for different analyses are also included, comprising: (i) a treatment of the stellar and ionized gas on individual spectra, spaxel by spaxel; (ii) an analysis of both the stellar and ionized gas using a spatial binning scheme; and (iii) a hybrid analysis, in which the stellar population is analyzed by adopting a binning scheme and the ionized gas is studied spaxel by spaxel. In addition, a different combination of stellar libraries is used to explore the stellar kinematics and to decouple the stellar and ionized gas components, as well as both a Gaussian fitting and a moment analysis for the exploration of the emission lines. The most similar approach to the one performed by pyPipe3D is the hybrid method, which uses a combination of the MILES stellar library for the kinematics and the MaStar SSP library for decoupling the stellar and ionized gas components (the HYB10-MILESHC-MASTARSSP data set in their nomenclature). We compare our DR17 results with this catalog.
Figure 21 shows the comparison of the stellar indices in common between the two data sets. In general, we find a good match between the DAP and pyPipe3D. The best agreement is found for D4000. For this parameter, the pyPipe3D value has been corrected by the scaling factor proposed by Gorgas et al. (1999), transforming the index derived from a flux density in units of wavelength to a flux density in units of frequency (see Section 4.1). For the DAP value, we adopt the average between the two listed parameters, D4000 and Dn4000, since this combination provides the best comparison with our results. There is a tight one-to-one relation between the two estimates, with ΔD4000 = −0.01 ± 0.07. For the remaining spectral indices, there is a clear correspondence between the DAP and pyPipe3D, following a distribution on top of the one-to-one relation (e.g., Fe5270) or just showing a systematic offset with respect to that relation. In the latter cases, the offsets range between −0.47 Å for Mgb and 0.21 Å for Hβ . The scatter around these relations is of the order of ∼0.6 Å, corresponding to ∼20%–30% of the typical index value.
Figure 21. Comparison between the set of stellar indices common to both our analysis and the MaNGA-DAP DR for the full DR17 sample. We adopt the same format as in Figure 19. From left to right, we compare the values derived at the effective radius for the Fe5270, Fe5335, and Mgb metallic indices (top panels), and the Hδ, Hβ, and D4000 age-sensitive indices (bottom panels). For Hδ, we adopt our Hd_Re_fit parameter and the HDeltaA parameter in the dapall file. For D4000, we scale our estimate by the factor proposed by Gorgas et al. (1999; Section 6.3), and for the DAP we adopt the average between D4000 and Dn4000. Details of the compared quantities are given in Section 5.3 and in the MaNGA DAP presentation article (Westfall et al. 2019).
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Standard image High-resolution imageThe main driver of the observed differences is not the definition of the passbands adopted to derive the indices, since a cross-check of our adopted values (Table 5) with the ones published in Westfall et al. (2019) indicates that there is no difference down to the third decimal. Therefore, the difference should be in the details of the procedure. For instance, the DAP adopts a different binning scheme than the one implemented by our code. Thus, the indices are not calculated for exactly the same spectra. For indices affected by the subtraction of the emission lines, like Hβ, there is an additional source of discrepancy, as the treatment of the emission lines is different in the DAP and pyPipe3D. However, both effects should contribute to the observed scatter, but we have found some clear systematic offsets (e.g., Mgb). We do not have a definitive explanation for these offsets, although we suspect that the preprocessing of the data performed as part of our analysis (Section 3), which involves the homogenization of the spectral resolution, may be behind the observed differences. In any case, despite the reported offsets and discrepancies, the similarities between both sets of indices are such that no significant differences should be introduced by using either the DAP or the pyPipe3D indices in further analysis.
Figure 22 shows the comparison of the same set of emission-line properties derived by the DAP and pyPipe3D for the central regions of the galaxies shown in Figure 20. In general, we find the same differences/similarities between the parameters derived using the DAP and pyPipe3D as when we compare the results of DR15 and DR17 (Section 6.2). In some cases, like FHα,cen and [S ii]/Hα, the systematic offset is smaller than the one found in the previous comparison. In other cases, like [O iii]/Hβ and [N ii]/Hα, they are slightly larger. However, in no case are the offsets significant compared with the standard deviation of the difference between the two sets of values. The larger differences are found for: (i) EW(Hα), for values lower than 1 Å (measured by pyPipe3D), a range for which the DAP predicts slightly larger values; (ii) Hα/Hβ ratio for values lower than 2.5 (measured by the DAP), a range for which pyPipe3D derives slightly larger values; (iii) FHα,cen, for values lower than 0.1 10−16 erg s−1 cm−2 (measured by pyPipe3D), a regime for which the DAP predicts slightly larger values; and (iv) the [S ii]/Hα ratio, for values larger than 0 dex (measured by pyPipe3D), where the DAP predicts slightly lower values. For cases (i) and (ii), the differences occur in regimes of very low intensity (and S/N) of the emission lines, where any measurement is unreliable. For case (iii), the difference is irrelevant in most of the calculations, since this line ratio is usually adopted to estimate the dust extinction (e.g., Section 5.3.5), and the considered regime corresponds to either unphysical values (Hα/H β < 2.5) or a regime of very low dust extinction. Finally, for case (iv), the difference is so small than it should not affect any further analysis. In summary, we consider that any analysis using the emission-line fluxes derived by both procedures should provide very consistent results.
Figure 22. Comparison between a set of ionized gas properties derived using pyPipe3D and those distributed as part of the MaNGA DAP DR for the full DR17 sample. We adopt the same format as in Figure 19. From left to right, and from top to bottom: Hα flux (FHα,cen), equivalent width of Hα (EWHα,cen), Hα-to-Hβ line ratio, [O iii]-to-Hα line ratio, [N ii]-to-Hα line ratio, and [S ii]-to-Hα line ratio, all of them derived for the central aperture. For details of these quantities, see Section 5.3 and the MaNGA DAP presentation article (Westfall et al. 2019).
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Standard image High-resolution imageThe final parameters in common between the DAP and pyPipe3D correspond to the kinematical properties of galaxies. Figure 23 shows the comparison of the stellar σ⋆ (left panel) and Hα (σHα ; central panel) velocity dispersions estimated within 1 Re. In both cases, there is good correspondence between the DAP and DR17 estimates. However, there are noticeable differences. In the case of σ⋆, the DAP values are slightly larger (∼12 km s−1), showing a systematic offset that is evident, even though it is smaller than the scatter (∼27 km s−1). Furthermore, there is a clear bend in the distribution for σ⋆,DR17 < 70 km s−1, where the DAP values reach a plateau at ∼50 km s−1, while the pyPipe3D values are still decreasing. These bends occur when there is a discrepancy in the treatment of the instrumental velocity dispersion as a consequence of a mismatch between the spectral resolution of the adopted SSPs or stellar templates and the observations. In our case, we use SSPs based on the MaStar library, and therefore they have a similar spectral resolution as the analyzed data. However, the DAP adopts a subset of the MILES stellar library for the kinematics analysis. Without prejudging which of the two approaches provides a more realistic estimate of the velocity dispersion, we just note that the difference in the adopted procedure may explain the observed differences at low σ⋆.
Figure 23. Comparison between the stellar (σ⋆; left panel) and Hα (σHα ; central panel) velocity dispersions derived using pyPipe3D and the values derived by the MaNGA DAP within 1 Re for the full DR17 sample. The right panel shows the distribution of σ⋆ as a function of M⋆, derived from our analysis of the RGs—those with ∣ EW(Hα)∣ < 3 Å at the effective radius. We adopt the same format as in Figure 19. In the right panel, with the dotted–dashed line, we include the FJ relation (Faber & Jackson 1976), as published by Aquino-Ortíz et al. (2018), for comparison purposes.
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Standard image High-resolution imageIn order to get an independent judgment of the quality of the pyPipe3D estimates of σ⋆, we compare them with the values expected from the stellar masses of early-type galaxies, as predicted by the well-known Faber–Jackson (FJ) relation (Faber & Jackson 1976). The right panel of Figure 23 shows the distribution of σ⋆ measured within the effective radius by pyPipe3D, together with the FJ relation estimated by Aquino-Ortíz et al. (2018) for the same aperture. We find that the DR17 results follow the FJ relation down to ∼60 km s−1, a value that roughly corresponds to the spectral resolution of the data. Thus, we consider that our DR17 values of σ⋆ are reliable at least above this limit. In order to provide a direct comparison between the two estimates of the velocity dispersion, we derive a prescription to transform from one to the other, by following the equation
![Equation (16)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn16.gif)
After applying this transformation, the two estimates of the velocity dipersion show a one-to-one relation, with Δσ⋆ = 0.3 ± 22 km s−1, a scatter similar to the expected error in this quantity (Lacerda et al. 2022).
Finally, for the ionized gas velocity dispersion, we find a systematic offset between the values reported by the DAP and pyPipe3D of ΔσHα = −16 ± 41 km s−1. We recall that our estimate is based on an analysis of the emission lines included in the FLUX_ELINES extension of the Pipe3D FITs file. In this extension, we include the velocity dispersion measured in angstroms (Table 7). We realized that for the published table we underestimated the instrumental dispersion by 45 km s−1, leading to the observed discrepancy. Therefore, for any further analysis, we recommend applying the following correction to the Hα velocity dispersion included in the final catalog:
![Equation (17)](https://content.cld.iop.org/journals/0067-0049/262/2/36/revision2/apjsac7b8feqn17.gif)
Once corrected, the two quantities agree within a standard deviation, ∼27 km s−1.
6.4. Values Reported by FIREFLY
The MaNGA DR17 data set has been analyzed using the FIREFLY (Wilkinson et al. 2017) full spectral fitting code to derive the main spatially resolved and stellar population properties of each individual galaxy. Like pyPipe3D, FIREFLY implements a chi-square minimization fitting code that combines a template of SSPs to model the observed spectra. A Bayesian information criterion for selecting the best-fitting model, without assuming any priors, is adopted. Beside this, the major difference with respect to pyPipe3D is the treatment of dust extinction. Although FIREFLY includes a method whereby extinction by dust is included as part of the stellar decomposition process, the default procedure involves the preprocessing of both the observed spectra and the SSP templates, which removes their shapes (over a bandwidth of ∼100 Å), and from this first best-fitted model the dust extinction is derived. This analysis, applied to MaNGA DR17, provides a full set of spatially resolved properties for the stellar populations, including the corresponding maps for LW and MW and
,
25
from which the values at the effective radius and the slopes of their radial gradients are extracted (Neumann et al. 2022).
26
Here, we compare the values derived for the LW parameters by FIREFLY and pyPipe3D. In this comparison, we should bear in mind that the FIREFLY data products were derived using: (i) a different SSP template (Maraston et al. 2020) than the one adopted in our analysis, although we selected results based on the same stellar library for this comparison (i.e., MaStar); (ii) the spatial binning and the results of the kinematical analysis derived by the DAP (Section 6.3); (iii) a different wavelength to weight the stellar populations—the average observed wavelength (∼6600 Å), instead of the rest-frame 5500 Å adopted by pyPipe3D; (iv) a different method to derive the LW values, as they adopted an arithmetic weighted average (according to Neumann et al. 2022, Equation (2), although this was not the case in previous versions of the code, e.g., Goddard et al. 2017); and (v) a different procedure to derive the radial gradients. As indicated in Section 5.3, our method excludes the central regions of the galaxies (<0.5 Re), since we consider that they are usually affected by PSF/beam effects. Furthermore, the gradient is derived up to 2.0 Re (or the maximum extension covered by the FOV). On the contrary, the values reported by FIREFLY are derived for the region within 1.5 Re. In both cases, an azimuthal average is performed (contrary to previous derivations of this parameter by FIREFLY, e.g., Figure 8 and Section 3.2 of Goddard et al. 2017).
Figure 24 compares the FIREFLY and pyPipe3D values of and
, measured at the effective radius, and the slopes of their corresponding gradients.
27
For age and metallicity, we observe a clear correspondence between the values reported from both methods, but not a one-to-one relation. On average, FIREFLY derives older stellar populations than pyPipe3D, a bias that is stronger for the younger stellar populations (with a difference of ∼0.5 dex) than for the older ones (with a difference of ∼0.1 dex). As a result, we find a positive offset of
dex, with a scatter of ∼0.25 dex. Both the offset and the scatter are larger than the values found in the comparison for the same parameter in Section 6.2, and larger than for any of our previous comparisons between this tool and other stellar population analysis techniques (e.g., Figure 17 and Section 4 of Sánchez et al. (2016a)). A similar pattern is observed for the metallicity: FIREFLY provides larger values than pyPipe3D, although in this case the offset seems to be similar (or at least of the same order) for low and high metallicities. On average, we find
0.21 ± 0.16 dex.
Figure 24. Comparison between the LW age () and metallicity (
) at the effective radius, together with their corresponding radial gradients (∇
and ∇
), derived by us and the FIREFLY MaNGA DR17 VAC values. We adopt the same format as Figure 19. The details of the compared quantities are given in Section 5.3 and in the FIREFLY presentation article (Goddard et al. 2017).
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Standard image High-resolution imageThe reported discrepancies are a consequence of the combined effects of the differences in the adopted procedures listed in the previous paragraph. Systematic differences are expected just due to the use of a different SSP library (e.g., the appendices in González Delgado et al. 2014, 2015; Sánchez et al. 2016b), differences that are enhanced by the adoption of a different binning scheme (e.g., Ibarra-Medel et al. 2019), a different method for deriving the mean values, and the normalization at a redder wavelength (which enhances the older and more metal-rich stellar populations). In any case, despite these quantitative differences, qualitatively both procedures classify the old (metal-rich) and young (metal-poor) populations in similar ways.
Stronger differences are found for the radial gradients (the right panels of Figure 24). The gradients derived using the two methods present a less clear correspondence to one another than the parameters discussed previously, with a scatter of the same order of the dynamical range covered by the gradients. The gradient presents a somehow better correspondence, with FIREFLY deriving slightly sharper gradients than pyPipe3D:
= −0.14 ± 0.27 dex/Re. On the contrary, the relation between the
gradients is very loose, with an average difference of
= 0.09 ± 0.13 dex/Re, and the FIREFLY gradients tending to be shallower.
We consider that the combination of the differences in the FIREFLY procedure outlined above, and, in particular, the use of a different scheme to derive the gradient, can easily explain the reported discrepancies between the FIREFLY and the pyPipe3D gradients. It is worth noticing that despite these differences, both methods predict a slightly sharper (shallower) gradient for both quantities at higher (lower) stellar masses, in agreement with previous results (e.g., González Delgado et al. 2015; Sánchez et al. 2021, and references therein). Thus, both methods provide similar qualitative results in a statistical sense, despite the large reported differences in the gradients for individual galaxies.
7. A Practical Use of the Catalog: AGN Selection
Sánchez et al. (2018) illustrated the use of the analysis performed with Pipe3D for the MaNGA DR14 data set by selecting the candidates for AGNs and exploring the properties of their host galaxies in comparison to the bulk population of nonactive galaxies. That analysis implied the exploration of many different integrated characteristics and resolved properties, which is beyond the scope of the present study. We present here an update of the AGN selection, using the pyPipe3D analysis for the final MaNGA DR, which covers five times more galaxies, and showing a few examples of the comparisons between active and inactive galaxies, as shown in that article.
Following Sánchez et al. (2018) and recent reviews of the topic (e.g., Sánchez 2020; Sánchez et al. 2021, and references therein), we select the candidates for hosting an AGN based on a combination of the use of classical diagnostic diagrams and a minimum value for the EW(Hα). We use the emission-line ratios derived for the 25/diameter central aperture, described in Section 5.3.5, to explore the distribution across the [O iii]/Hβ versus [N i]/Hα (O3N2), [S ii]/Hα (O3S2), and [O i]/Hα (O3O1) diagnostic diagrams. We then select as AGN candidates those objects located above the Kewley et al. (2001) demarcation line (K01) in the three diagrams simultaneously and with EW(Hα) > 3 Å.
For the full sample of ∼10,000 galaxies, only ∼180 objects present no evidence of ionized gas in the central region in at least one of the analyzed emission lines. Thus, in the vast majority of the galaxies, we find evidence of ionized gas even in the central aperture, although in many cases at a very low S/N. Selecting only those objects with a detection of Hα in the central aperture with an S/N > 10, and an S/N > 1 for the remaining emission lines involved, we restrict the sample to ∼7000 galaxies. Of these, only ∼1700, ∼1500, and ∼1200 objects are located above the K01 demarcation lines in the O3N2, O3S2, and O3O1 diagnostic diagrams, respectively. Limiting the sample to those objects common to the three subsets, and imposing the minimum cut in EW(Hα), we end up with 224 AGN candidates. By construction, they correspond to optically selected type-II AGNs. Following Sánchez et al. (2018), we perform an automatic search for the presence of a broad emission-line component in the emission line of Hα. This procedure allows us to recover some possible type-I AGNs that have not been selected by the previous criteria. The implemented algorithm maximizes the detection of any broad component. Therefore, it is required to impose a cut in the S/N of this component for a proper type-I selection. This additional analysis recovers three more targets that were not included in the selection described before. In summary, we select a final sample of 227 candidates for hosting an AGN. From this sample, we find possible evidence of a broad component in 119, but only for nine of them can we recover this broad component with an S/N > 10.
Figure 25 shows the distribution across the three diagnostic diagrams considered for the values used in the AGN selection, color coded by the EW(Hα). As expected, on average, the objects with high EWs (>6 Å) are found below the demarcation lines that are usually adopted to select the ionization due to young OB stars, which trace recent SF activity (e.g., Figure 10), in particular below the Kauffmann et al. (2003a) demarcation line. On the contrary, the objects above those lines are those that in general present low EW(<3 Å), in particular those above the K01 demarcation line. This bimodality is evident in the histogram of the EWs shown in the inset, where the valley between the two peaks at low and high EWs is located at 6 Å, broadly separating the SF from the non-SF regions. These results are in agreement with the ones discussed in Section 5.2 as well as the current understanding of the dominant ionization sources in galaxies.
Figure 25. Distributions of [O iii]/Hβ vs. [N ii]/Hα (left panel), [S ii]/Hα (central panel), and [O i]/Hα (right panel), corresponding to the central aperture measurements for all the galaxies, color coded by the average EW(Hα) in logarithmic scale in the same aperture (with its histogram, also in logarithmic scale, shown as an inset in the left panel, including the mean value: ∼5 Å). The contours correspond to the density of the points, with the first one encircling 80% of the data, and each consecutive one then encircling 20% fewer points. The stars correspond to the AGNs selected, as described in the text, color coded by EW(Hα) in the case of type-II candidates and by the use of a yellow color in the case of type-I candidates. In each panel, the dashed–dotted lines correspond to the K01 demarcation line, and the dotted line in the left panel corresponds to the Kauffmann et al. (2003a) demarcation line. Those lines are frequently used to separate between SF-like and AGN-like ionization. The diagonal line in the top right quadrant corresponds to the separation between Seyferts and LINERs proposed by Kewley et al. (2001), too.
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Standard image High-resolution imageThe AGN candidates are distributed above the demarcation lines, having large values of EW(Hα) by selection; thus, they are objects that are clearly not following the general trend. The few type-I AGNs are also distributed about the demarcation lines. However, in three cases they do not fulfill the EW criteria. This is not surprising, since those EWs are derived using the moment analysis, which does not implement a detailed deblending between broad and narrow lines.
Once we have selected the candidates for hosting AGNs, we can compare their properties with those of nonactive galaxies. Figure 26 shows the distributions of ,
, and stellar masses, segregated by morphology, as violin plots for both the AGN hosts and the inactive galaxies. cD galaxies have been excluded for the reasons explained in Section 6.1. For the general population of galaxies, we note the well-known trends between morphology, stellar mass, and the age and metallicity of the stellar populations (e.g., García-Benito et al. 2017). In agreement with the most recent results based on similar selections (e.g., Lacerda et al. 2020, and references therein), host galaxies are found in the range of high stellar masses (with M⋆ > 1010
M⊙, in most of the cases, and ∼1011
M⊙ on average), with metal-rich and intermediate-to-old stellar populations being absent from galaxies without a clear bulge (i.e., later than Sc). Indeed, these two properties—high stellar mass and the presence of a bulge—seem generally to be equally important for hosting an AGN.
Figure 26. Violin plot of the (top panel),
(middle panel), and stellar masses (bottom panel), segregated by morphology (with cD galaxies excluded; see Section 6.1), for the complete sample (light gray) and the AGN hosts (dark gray). Histograms of the number of galaxies segregated by morphology and mass for both subsamples have been included in the top and right panels, respectively. In both cases, a logarithmic scale was adopted for the histograms, to highlight the bins with low numbers.
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Standard image High-resolution imageFigure 27 shows the distribution of the full sample of galaxies across the SFR–M⋆ diagram, color coded by the EW(Hα) measured at the effective radius of the galaxies, with the locations of the selected AGN hosts highlighted. As expected, the galaxies with high EW, which essentially correspond to SFGs, follow a well-defined linear distribution across this diagram, i.e., the so-called SF main sequence (e.g., Brinchmann et al. 2004; Renzini & Peng 2015; Cano-Díaz et al. 2019). On the contrary, the galaxies with low EW, which are not actively forming stars (RGs; Stasińska et al. 2008), are distributed in a lousy cloud well below this sequence. Again, this distribution reflects a well-known bimodality observed in galaxies, which involves several properties, including not only the star formation stage, but also the colors, morphology, dynamical stage, and gas content, among several others (e.g., Blanton et al. 2017).
Figure 27. Distribution of galaxies along the SFR–M⋆ diagram, color coded by the average EW(Hα) at the effective radius. For the SFR, we adopted the derived value based on the dust-corrected Hα luminosity, and for the stellar mass we adopted the value derived from the stellar analysis performed by pyPipe3D. The contours correspond to the density of the points, with the first one encircling 80% of the data, and each consecutive one encircling 20% fewer points. The stars corresponds to the AGNs selected as described in the text, color coded by the EW(Hα) at the effective radius in the case of type-II candidates and by the use of a yellow color in the case of type-I candidates.
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Standard image High-resolution imageIn between the two peaks in density, defined by the SFGs and RGs, there is a region with a relative dearth of galaxies (see the inset in Figure 25), which is usually called the GV. The GV was first described for the color–magnitude diagram (e.g., Kauffmann et al. 2003c), in which the RGs follow a red sequence and the SFGs a loose blue cloud. However, this valley is more easily identified in the SFR–M⋆ diagram, where the distinction between SFGs and RGs is sharper. The GV is supposed to be a transition zone between the star formation stage and the retired stage of galaxies. Contrary to the main population of nonactive galaxies, AGN hosts do not present a bimodal distribution in either the color–magnitude or the SFR–M⋆ diagrams. In both cases, they are preferentially found in the GV, with a footprint that covers the edges of the SF main sequence (the red sequence) toward the red (blue) cloud for the SFR–M⋆ (color–magnitude) diagram (e.g., Kauffmann et al. 2003a; Sánchez et al. 2004; Schawinski et al. 2014). Sánchez et al. (2018) confirmed this result by exploring the data products of Pipe3D for the MaNGA DR14 data set, avoiding the problems introduced by the mixing of ionizing sources, due to single-aperture spectroscopic observations, like the ones provided by the original SDSS survey. Figure 27 corroborates this result by using the current updated data set. It is clearly observed that the distribution of AGN hosts in the SFR–M⋆ diagram peaks at the GV: the vast majority of them are constrained in a region between the location of the SFMS (traced by the peak in the density distribution of SFGs) and an SFR approximately one order of magnitude below this location. We find some AGNs with extreme SFRs, with respect to the average population, but there are just low numbers of them (two or three out of 200).
In summary, for the two explored distributions, we find very similar results for the current data set with the ones already published in Sánchez et al. (2018), using a more limited sample (a five times fewer number of galaxies). The updated list of AGN candidates is accessible online 28 for further exploration. In this table, we include the complete list of AGNs. The relevant entries for selecting between type-I and type-II AGNs are the agn_type entry (1 for objects with some evidence of a broad component in Hα, 2 for objects with no evidence of such a component) and sn_Ha_broad (the S/N of the broad Hα component). For the current discussion, we have classified the AGNs as type-I if agn_type is equal to 1 and sn_Ha_broad is larger than 10. On the contrary, the AGNs are classified as type-II.
8. Summary and Conclusions
In this article, we present the data products derived from the analysis performed using the pyPipe3D pipeline on the full MaNGA data set, comprising ∼10,000 IFS data cubes, for a similar number of galaxies. We briefly describe the sample of galaxies, the observing technique, and the data reduction. We explain in detail the implemented analysis, including a summary of the procedures that comprise the pyPipe3D pipeline, highlighting the differences between the previous version of the code (Pipe3D), when needed. We describe the new SSP library and a set of additional analyses that were performed, including: (i) a statistical estimate of the morphological types of the galaxies; (ii) photometric and structural analyses; and (iii) the procedures adopted to estimate the quality of the data.
As a result of this analysis, we deliver to the community one of the largest data sets of fully analyzed IFS data, comprising: (i) a single FITs file for each analyzed data cube in the Pipe3D format; and (ii) a catalog of ∼500 integrated and characteristic properties for each object. The data model of the Pipe3D file is described in detail, including explanations of the content of each of the different extensions, the spatially resolved data products, the procedures for deriving them, their units, and their uncertainties. An example of the content of each extension is included, using an arbitrarily selected target (the spiral galaxy manga-7495-12704) as a showcase. Particular care has been taken to describe the newly included extensions, like the ELINES and FLUX_ELINES_LONG ones. Using the complete set of Pipe3D files, we explore the distribution across the classical [O iii]/Hβ versus [N ii]/Hα diagnostic diagram of all the spatially resolved ionized regions, segregating them by the stellar mass and morphology of their host galaxies, and finding consistent results with previous publications (Sánchez 2020). This showcase example demonstrates the scientific use of the spatially resolved data products delivered.
A detailed description of the individual parameters derived for each galaxy included in the delivered catalog, comprising both integrated and characteristic properties, is included. A clear distinction has been established between parameters inherited from previous tables (included to facilitate the identification of the targets in the sky and in other catalogs or as being part of the quality control procedure) and parameters derived as part of our analysis. We explain the derivation of the parameter values at different apertures (integrated, central, and at the effective radius) and the derivation of the corresponding slopes for the radial gradients (when required). We present separate descriptions of the properties derived for the stellar population, the emission lines, and the kinematic properties, indicating when needed the Pipe3D file extension to which they belong. In addition, we describe the delivered results from our morphological analysis and the update to the volume corrections presented in Sánchez et al. (2019a). In all cases, we clearly state the entry in the final catalog that corresponds to each of the derived and delivered quantities.
Our set of estimated parameters and properties has been compared with similar sets that are already publicly available. The main results from this comparison are:
- 1.Our proposed morphological classification is statistically similar to the DS22 classification, with very consistent results for all the morphological types in common.
- 2.The comparison of our results with the galaxy properties estimated using (i) a previous version of the Pipe3D pipeline for the MaNGA DR15 data set (∼4500 galaxies in common) and (ii) the MaNGA DAP for the DR17 data set show very good agreement in most of cases. On the contrary, the comparison with the FIREFLY results shows a poorer degree of agreement, depending on the parameter.
- 3.The larger differences are found for those properties of the stellar populations that depend more strongly on the SSP template adopted for the stellar decomposition. Comparing with the DR15 results, we find very good agreement for M⋆, ϒ⋆, SFRssp, σ⋆,cen,
, and
, a systematic offset for
, and a deviation from the one-to-one relation toward an asymptotic lower value (in the case of the DR15 data) for
, as expected, due to the lower dynamical range in the metallicity of the DR15 SSP library. Finally, the dust extinction (A⋆,V) presents slightly larger values in the DR15 data set (although within the observed dispersion).
- 4.In the case of FIREFLY, we find deviations from the one-to-one relation for both
and
, although in this case the metallicity seems to present a systematic offset and the age a linear relation (with a slope different than 1). The largest discrepancies occur for the slopes of the radial gradients of both parameters, which show only a mild correspondence.
- 5.Considerably good agreement is found between our estimates of the spectral indices and those reported by the DAP. In some cases, there is a systematic offset, but in general the degree of agreement is similar to the one found when comparing repeated observations of the same object analyzed using the same tool.
- 6.The best agreement when comparing the Pipe3D DR15 and the DAP DR17 data sets is found for the emission-line properties, especially when FHα,cen > 10−17 erg s−1 cm−2, ∣EW(Hα)∣ > 0.5 Å. This is the regime in which the uncertainties (or differences) due to the subtraction of the stellar population are less relevant. The only parameter for which we find a significant deviation from the one-to-one relation is AV, gas, which shows a systematic offset, with the DR15 values being ∼0.24 mag higher than those for DR17.
- 7.Finally, we find a systematic offset in
and a deviation from the one-to-one relation in
between our estimates and those reported by the DAP. We consider that an error in our estimates of the first parameter, and the differences in the adopted template of the stellar spectra in the second parameter, are the most suitable cause of these differences.
As a practical example of the use of our final catalog, we update our selection of AGN host candidates, following Sánchez et al. (2018). We select the candidates using the emission-line properties extracted from the central apertures of the data cubes, adopting criteria that combine explorations of their locations in three diagnostic diagrams with a cut in EW(Hα). In addition, we look for evidence of a broad component in Hα in order to detect possible type-I AGNs. As a result of this analysis, we find a total of 227 AGN host candidates, nine of them being candidates for hosting a type-I AGN. A brief comparison between the properties of this set of candidates with the properties of nonactive AGNs confirms the main results from previous studies, suggesting that these objects are located in the transition regime between SFGs and RGs (e.g., Kauffmann et al. 2003a; Sánchez et al. 2004; Schawinski et al. 2014; Sánchez et al. 2018).
In summary, we consider that the current delivered analysis comprises a unique data set for the exploration of the spatial, integrated, and characteristic properties of galaxies in the nearby universe.
We thank the referee for the suggestions and comments that have improved this article and helped us to clean it.
We thank S. Charlot for his contribution to generating the SSP templates. We would like to thank J. Neumann and H. Domínguez-Sánchez for the comments that have improved this article.
S.F.S. and J.B.-B. are grateful for the support of CONACYT grants CB-285080 and FC-2016-01-1916, and funding from the PAPIIT-DGAPA-IN100519 and IG100622 (UNAM) projects. J.B.-B. acknowledges support from CONACYT grant CF19-39578. G.B. acknowledges financial support from the National Autonomous University of México (UNAM) through grant DGAPA/PAPIIT BG100622.
This research made use of Astropy, 29 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018).
Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions.
SDSS-IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is www.sdss.org.
SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration, including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics ∣ Harvard & Smithsonian, the Chilean Participation Group, the French Participation Group, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.
As indicated before, the full data products and final catalog produced by the pipeline are freely distributed to the community: http://ifs.astroscu.unam.mx/MaNGA/Pipe3D_v3_1_1/. The MaNGA DR17 reduced data set analyzed throughout this article is available via the SDSS DR17 web page: https://www.sdss.org/dr17/manga/.
Appendix A: The C&B SSP Models
A major revision of the BC03 stellar population synthesis models was introduced in Plat et al. (2019; hereafter, the Charlot and Bruzual (C&B) models). Even though the C&B models have been used by several authors (e.g., Mayya et al. 2020; Werle et al. 2020; González Delgado et al. 2021; Senchyna et al. 2021; Orozco-Duarte et al. 2022; Senchyna et al. 2022; Werle et al. 2022), a detailed description of the ingredients of these models is lacking in the literature. For completeness, and for the benefit of the reader, we summarize in this appendix the characteristics of these C&B models.
The C&B models follow the PARSEC evolutionary tracks (Marigo et al. 2013; Chen et al. 2015) for the 16 chemical compositions listed in Table 8. The initial solar nebula had Z = 0.014 and the current Sun has a surface abundance of Z = 0.017. These state-of-the-art evolutionary tracks follow the evolution of stars with masses from 0.1 to 600 M⊙, using a fine grid of mass and time steps. The tracks run from the main sequence to the Wolf–Rayet (WR) phase for massive stars and up to the thermally pulsing asymptotic giant branch (TP-AGB) for stars below 6 M⊙. In the C&B models, we follow the evolution of the pAGB phase of intermediate- and low-mass stars, according to Miller Bertolami (2016). A major source of uncertainty in building these models is the lack of spectra, either theoretical or empirical, for stars of all the metallicities listed in Table 8 at all evolutionary phases. For illustrative purposes, the check marks in Table 9 indicate the nominal values of [Z/Z⊙] for which the different theoretical atlases listed in the table have been computed. To build sensible population synthesis models, it is then necessary to make compromises on what stellar spectra to use for each set of tracks.
Table 8. PARSEC Tracks Used in the C&B Models
X | Y | Z | Z/Z⊙ | [Z/Z⊙] |
---|---|---|---|---|
0.5840 | 0.3560 | 0.060 | 3.529 | 0.55 |
0.6390 | 0.3210 | 0.040 | 2.353 | 0.37 |
0.6680 | 0.3020 | 0.030 | 1.764 | 0.25 |
0.6960 | 0.2840 | 0.020 | 1.176 | 0.07 |
0.7040 | 0.2790 | 0.017 | 1.000 | 0.00 |
0.7130 | 0.2730 | 0.014 | 0.824 | −0.08 |
0.7230 | 0.2670 | 0.010 | 0.588 | −0.23 |
0.7290 | 0.2630 | 0.008 | 0.471 | −0.33 |
0.7350 | 0.2590 | 0.006 | 0.353 | −0.45 |
0.7400 | 0.2560 | 0.004 | 0.235 | −0.63 |
0.7460 | 0.2520 | 0.002 | 0.118 | −0.93 |
0.7490 | 0.2500 | 0.001 | 0.059 | −1.23 |
0.7505 | 0.2490 | 0.0005 | 0.029 | −1.53 |
0.7508 | 0.2490 | 0.0002 | 0.012 | −1.93 |
0.7509 | 0.2490 | 0.0001 | 0.006 | −2.23 |
0.7700 | 0.2300 | 0.0000 | 0.000 | −∞ |
References. Chen et al. (2015); Marigo et al. (2013). We use tracks computed for [α/Fe] = 0.
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Table 9. [Z/Z⊙] of the Model Spectra Available in Different Atlases
[Z/Z⊙] | Tlusty a | Tlusty b | Martins c | UVBlue d | Rauch e | WMBasic f | PoWR g | Aringer h | BaSeL i |
---|---|---|---|---|---|---|---|---|---|
O | B | A | F,G,K | T > 50 kK | Hot MS | WR | C stars | 3.1 | |
+0.50 | ✓ | ✓ | |||||||
+0.30 | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
+0.00 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
−0.30 | ✓ | ✓ | |||||||
−0.40 | ✓ | ✓ | |||||||
−0.50 | ✓ | ✓ | ✓ | ✓ | |||||
−0.70 | ✓ | ✓ | ✓ | ✓ | |||||
−1.00 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
−1.15 | ✓ | ||||||||
−1.30 | ✓ | ||||||||
−1.50 | ✓ | ✓ | ✓ | ||||||
−1.70 | ✓ | ||||||||
−2.00 | ✓ | ✓ | ✓ | ||||||
−3.00 | ✓ | ||||||||
− ∞ | ✓ | ✓ |
Notes. We use stellar models computed for [α/Fe] = 0.
(a)Lanz & Hubeny (2003a, 2003b). (b)Lanz & Hubeny (2007). (c)Martins et al. (2005). (d)Rodríguez-Merino et al. (2005). (e)Rauch (2003). (f)Leitherer et al. (2010). (g)Gräfener et al. (2002), Hamann & Gräfener (2003), Hamann et al. (2006), Sander et al. (2012), Hainich et al. (2014), Hainich et al. (2015), Todt et al. (2015). (h)Aringer et al. (2009). (i)Westera et al. (2002).Download table as: ASCIITypeset image
Table 10 indicates the values of [Z/Z⊙] of the theoretical atlases or the range of [Z/Z⊙] for the stellar libraries used to build each population synthesis model. In Table 11, we list the spectral properties of the different theoretical models used in the UV spectral range. Finally, Table 12 indicates the stellar atlas(es) used in the population synthesis models versus wavelength range. From Table 12, we see that in the visible range the standard C&B models use the MILES library, from 3540.5 to 7350.2 Å, and the IndoUS library, from 7350.2 to 9399.8 Å. Versions of these models were computed using the Stelib library, for comparison with the BC03 models. The C&B models were computed for the Chabrier (2003), Salpeter (1955), and Kroupa (2001) IMFs for MUP = 100,300 and 600 M⊙. Each SSP model provides 220 spectra computed at different time steps, ranging from 0 to 14 Gyr.
Table 10. [Z/Z⊙] of the Different Stellar Atlases Assigned to Each PARSEC Track in the C&B Models
Tlusty | Tlusty | Martins | UVBlue | Raucha | WMBasic | PoWR | Aringer | BaSeL a | MILES b /IndoUS c | Stelib d | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Z | Z/Z⊙ | O | B | A | F,G,K | T>50kK | Hot MS | WR | C stars | 3.1 | Libraries | Library |
0.060 | 0.55 | +0.30 | +0.30 | +0.30 | +0.50 | +0.00 | +0.30 | +0.00 | +0.00 | +0.50 | [+0.1, +0.6) | >0.06 |
0.040 | 0.37 | +0.30 | +0.30 | +0.30 | +0.30 | +0.00 | +0.30 | +0.00 | +0.00 | intrp | [+0.1, +0.6) | >0.06 |
0.030 | 0.25 | +0.30 | +0.30 | +0.30 | +0.30 | +0.00 | +0.30 | +0.00 | +0.00 | intrp | [+0.1, +0.6) | >0.06 |
0.020 | 0.07 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | intrp | [−0.1, +0.1) | [−0.06, +0.06] |
0.017 | 0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | +0.00 | [−0.1, +0.1) | [−0.06, +0.06] |
0.014 | −0.08 | +0.00 | +0.00 | +0.00 | +0.00 | intrp | +0.00 | +0.00 | +0.00 | intrp | [−0.1, +0.1) | [−0.06, +0.06] |
0.010 | −0.23 | −0.30 | −0.30 | −0.50 | −0.50 | intrp | −0.40 | −0.40 | +0.00 | intrp | [−0.6, −0.1) | [−0.48, −0.06) |
0.008 | −0.33 | −0.30 | −0.30 | −0.50 | −0.50 | intrp | −0.40 | −0.40 | −0.50 | intrp | [−0.6, −0.1) | [−0.48, −0.06) |
0.006 | −0.45 | −0.70 | −0.70 | −0.50 | −0.50 | intrp | −0.40 | −0.40 | −0.50 | −0.50 | [−1.3, −0.6) | [−0.92, −0.48) |
0.004 | −0.63 | −0.70 | −0.70 | −0.50 | −0.50 | intrp | −0.70 | −0.70 | −0.50 | intrp | [−1.3, −0.6) | [−0.92, −0.48) |
0.002 | −0.93 | −1.00 | −1.00 | −1.00 | −1.00 | −1.00 | −0.70 | −0.70 | −1.00 | −1.00 | [−1.3, −0.6) | <−0.92 |
0.001 | −1.23 | −1.50 | −1.00 | −1.00 | −1.00 | blbdy | −1.30 | −1.00 | −1.00 | intrp | [−1.3, −0.6) | <−0.92 |
0.0005 | −1.53 | −1.70 | −1.00 | −1.00 | −1.50 | blbdy | −1.30 | −1.00 | −1.00 | −1.50 | <−1.3 | <−0.92 |
0.0002 | −1.93 | −2.00 | −1.00 | −1.00 | −2.00 | blbdy | −1.30 | −1.00 | −1.00 | intrp | <−1.3 | <−0.92 |
0.0001 | −2.23 | −2.00 | −1.00 | −1.00 | −2.00 | blbdy | −1.30 | −1.00 | −1.00 | −2.00 | <−1.3 | <−0.92 |
0.0000 | − ∞ | − ∞ | − ∞ | −1.00 | −2.00 | blbdy | −1.30 | −1.00 | −1.00 | −2.00 | <−1.3 | <−0.92 |
Notes. We use stellar models computed for [α/Fe] = 0.
(a)Entries marked "intrp" indicate that the model is interpolated at the corresponding [Z/Z⊙]. Entries marked "blbdy" indicate that a blackbody curve is used to approximate the stellar emission. (b)Sánchez-Blázquez et al. (2006), Falcón-Barroso et al. (2011), Prugniel et al. (2011). (c)Valdes et al. (2004). (d)Le Borgne et al. (2003).Download table as: ASCIITypeset image
Table 11. Spectral Properties of the Different Stellar Atlases Relevant in the UV
Stellar | Stellar | Wavelength | |
---|---|---|---|
Library | Type | Range | R STEP = λ/Δλ |
TLUSTY | O stars | 45Å–300μm | 26,000–38,000 |
TLUSTY | B stars | 54Å–300μm | 100,000–200,000 |
Martins et al. | A stars | 3000–7000 Å | 10,000–23,000 |
UVBlue | F,G,K stars | 850–4700 Å | 50,000 |
Rauch | T > 50kK | 5–2000 Å | 50–20,000 |
WMBasic | Hot MS | 900–1500 Å | 2,040 |
WMBasic | Hot MS | 1500–2998 Å | 3,860 |
PoWR | WR stars | 5Å–8μm | 10,000 |
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Table 12. Wavelength Coverage versus Spectral Atlas Adopted in the C&B Models
Wavelength | Sampling | FWHM | Points | |||
---|---|---|---|---|---|---|
Range (Å) | Step (Å) | Δλ (Å) | N | Acum | % | Stellar Libraries |
5.6–911 | 0.9 | 2.0 | 1007 | 1007 | 6.0 | Tlusty, Martins et al., UVBlue, Rauch, WMBasic, PoWR |
911–3540.5 | 0.5 | 1.0 | 5259 | 6266 | 31.1 | Tlusty, Martins et al., UVBlue, Rauch, WMBasic, PoWR |
3540.5–7350.2 | 0.9 | 2.5 | 4233 | 10499 | 25.0 | MILES |
7350.2–9399.8 | 0.4 | 1.2–1.0 | 5124 | 15623 | 30.3 | IndoUS |
9410–36,000 μm | Variable | Variable | 1279 | 16902 | 7.6 | BaSeL 3.1, Aringer et al., IRTF libray a , + Dusty models for TP-AGB stars b |
Notes.
(a)Rayner et al. (2009). (b)Nenkova et al. (2000); González-Lópezlira et al. (2010).Download table as: ASCIITypeset image
A.1. The MaStar Variation of the C&B Models
We use the first release of MaStar (Yan et al. 2019) to build a test set of the C&B SSP models. This release of the MaStar library contains 8646 spectra of 3321 unique stars. The spectra cover the wavelength range from 3622 to 10354 Å, at a resolving power of R ≈ 1800. Due to the lack of a uniform calibration of the astrophysical parameters for the stars in this library, we opted for a quick and simple manner when assigning a MaStar spectrum to each star in the synthesis model. For each MILES spectrum used in our spectral synthesis, we searched for the closest matching spectrum (in log flux) in the MaStar library. We then replaced the MILES and IndoUS spectra (see lines 3 and 4 in Table 12) with the selected MaStar spectrum, over the full range covered by the latter. We checked that there were no systematic differences between the colors and the line strength indices computed for both sets of models as a function of time. The MaStar SSP models were computed for all the metallicities listed in Table 8. As its MILES counterpart, each MaStar SSP model contains 220 spectra at time steps from 0 to 14 Gyr. The subset of the MaStar C&B templates adopted throughout this article in the pyPipe3D format are accesible through the web. 30
Recently, Mejia-Narvaez et al. (2022) have determined the stellar atmospheric parameters for the latest release of MaStar, comprising over 22 k unique stars. At the moment, we are working on the implementation of this fully calibrated MaStar library in the C&B models.
Appendix B: Emission Lines Included in the FLUX_ELINES and FLUX_ELINES_LONG Extensions
The list of emission lines whose properties, derived using the moment analysis, are included in the FLUX_ELINES and FLUX_ELINES_LONG extensions are listed in Tables 13 and 14. For each emission line, we provide the running index I, described in Table 7, which allows the identification of each emission line with each of the properties stored in the different channels of the considered extensions. In addition, we list each of the adopted wavelengths and the labels that designate each of the emission lines. For the FLUX_ELINES extension, the wavelengths are based on the compilation presented in Sánchez et al. (2016a), while for the FLUX_ELINES_LONG extension, we extracted the wavelengths from the detailed list presented by Fesen & Hurford (1996).
Table 13. Emission Lines Included in the FLUX_ELINES Extensions
I | λ (Å) | Id | I | λ (Å) | Id |
---|---|---|---|---|---|
0 | 3727.4 | [O ii]3727 | 29 | 4889.62 | [Fe ii] |
1 | 3750.0 | H12 | 30 | 4905.34 | [Fe ii] |
2 | 3771.0 | H11 | 31 | 5111.6299 | [Fe ii] |
3 | 3798.0 | H10 | 32 | 5158.7798 | [Fe ii] |
4 | 3819.4 | HeI3819 | 33 | 5199.6001 | [N i] |
5 | 3835.0 | H9 | 34 | 5261.6201 | [Fe ii] |
6 | 3869.0 | [Ne iii] | 35 | 5517.71 | [Cl iii] |
7 | 3889.0 | H8 | 36 | 5537.6 | [Cl iii] |
8 | 3967.0 | [Ne iii] | 37 | 5554.94 | O i |
9 | 3970.1 | H![]() | 38 | 5577.3101 | [O i] |
10 | 4026.29 | HeI4026 | 39 | 5754.52 | [N ii] |
11 | 4069.17 | [S ii] | 40 | 5875.62 | HeI5876 |
12 | 4076.72 | [S ii] | 41 | 6300.2998 | [O i] |
13 | 4101.74 | Hδ | 42 | 6312.4 | [S iii] |
14 | 4276.83 | [Fe ii] | 43 | 6347.28 | Si ii |
15 | 4287.4 | [Fe ii] | 44 | 6363.7798 | [O i] |
16 | 4319.62 | [Fe ii] | 45 | 6562.68 | Hα |
17 | 4340.47 | Hϒ | 46 | 6583.41 | [N ii]6584 |
18 | 4363.21 | [O iii]4363 | 47 | 6548.08 | [N ii]6548 |
19 | 4413.78 | [Fe ii] | 48 | 6677.97 | HeI6678 |
20 | 4416.27 | [Fe ii] | 49 | 6716.39 | [S ii]6717 |
21 | 4471.0 | HeI4471 | 50 | 6730.74 | [S ii]6731 |
22 | 4657.93 | [Fe iii] | 51 | 7136.0 | [Ar iii] |
23 | 4686.0 | He ii | 52 | 7325.0 | [O ii] |
24 | 4712.98 | HeI4713 | 53 | 7751.0 | [Ar iii] |
25 | 4922.16 | HeI4922 | 54 | 9068.6 | [S iii] |
26 | 5006.84 | [O iii]5007 | 55 | 9530.6 | [S iii] |
27 | 4958.91 | [O iii]4959 | 56 | ⋯ | ⋯ |
28 | 4861.32 | Hβ |
Note. "I" corresponds to the index in Table 7, and "Id" is the label of each of the emission lines included in the FLUX_ELINES extension.
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Table 14. Emission Lines Included in the FLUX_ELINES_LONG Extensions
#I | λ (Å) | Id | #I | λ (Å) | Id | #I | λ (Å) | Id | #I | λ (Å) | Id | #I | λ (Å) | Id |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 3686.83 | H i | 39 | 4416.27 | [Fe ii] | 78 | 5039.1 | [Fe ii] | 117 | 6087.0 | [Fe vii] | 156 | 8300.99 | [Ni ii] |
1 | 3691.56 | H i | 40 | 4452.11 | [Fe ii] | 79 | 5072.4 | [Fe ii] | 118 | 6300.3 | [O i]* | 157 | 8308.39 | [Cr ii] |
2 | 3697.15 | H i | 41 | 4457.95 | [Fe ii] | 80 | 5107.95 | [Fe ii] | 119 | 6312.06 | [S iii] | 158 | 8345.55 | H i |
3 | 3703.85 | H i | 42 | 4470.29 | [Fe ii] | 81 | 5111.63 | [Fe ii] | 120 | 6363.78 | [O i] | 159 | 8357.51 | [Cr ii] |
4 | 3711.97 | H i | 43 | 4471.48 | He i | 82 | 5145.8 | [Fe vi] | 121 | 6374.51 | [FeX] | 160 | 8359.0 | H i |
5 | 3726.03 | [O ii]* | 44 | 4474.91 | [Fe ii] | 83 | 5158.0 | [Fe ii] | 122 | 6435.1 | [Ar v] | 161 | 8374.48 | H i |
6 | 3728.82 | [O ii]* | 45 | 4485.21 | [Ni ii] | 84 | 5158.9 | [Fe vii] | 123 | 6548.05 | [N ii]* | 162 | 8392.4 | H i |
7 | 3734.37 | H i* | 46 | 4562.48 | [Mg i] | 85 | 5176.0 | [Fe vi] | 124 | 6562.85 | Hα* | 163 | 8446.0 | O i |
8 | 3750.15 | H i | 47 | 4571.1 | Mg i] | 86 | 5184.8 | [Fe ii] | 125 | 6583.45 | [N ii]* | 164 | 8467.25 | H i |
9 | 3758.9 | [Fe vii] | 48 | 4632.27 | [Fe ii] | 87 | 5191.82 | [Ar iii] | 126 | 6678.15 | He i | 165 | 8498.02 | Ca ii |
10 | 3770.63 | H i | 49 | 4658.1 | [Fe iii] | 88 | 5197.9 | [N i]* | 127 | 6716.44 | [S ii]* | 166 | 8502.48 | H i |
11 | 3797.9 | H i* | 50 | 4685.68 | He ii | 89 | 5200.26 | [N i]* | 128 | 6730.82 | [S ii]* | 167 | 8542.09 | Ca ii |
12 | 3819.61 | He i | 51 | 4701.62 | [Fe iii] | 90 | 5220.06 | [Fe ii] | 129 | 6855.18 | FeI | 168 | 8545.38 | H i |
13 | 3835.38 | H i | 52 | 4711.33 | [Ar iv] | 91 | 5261.61 | [Fe ii] | 130 | 7005.67 | [Ar v] | 169 | 8578.7 | [ClII] |
14 | 3868.75 | [Ne iii] | 53 | 4713.14 | He i | 92 | 5268.88 | [Fe ii] | 131 | 7065.19 | He i | 170 | 8598.39 | H i |
15 | 3888.65 | He i* | 54 | 4724.17 | [NeIV] | 93 | 5270.3 | [Fe iii] | 132 | 7135.8 | [Ar iii]* | 171 | 8616.96 | [Fe ii] |
16 | 3889.05 | H i* | 55 | 4733.93 | [Fe iii] | 94 | 5273.38 | [Fe ii] | 133 | 7155.14 | [Fe ii] | 172 | 8662.14 | Ca ii |
17 | 3933.66 | Ca ii | 56 | 4740.2 | [Ar iv] | 95 | 5277.8 | [Fe vi] | 134 | 7171.98 | [Fe ii] | 173 | 8665.02 | H i |
18 | 3964.73 | He i* | 57 | 4754.83 | [Fe iii] | 96 | 5296.84 | [Fe ii] | 135 | 7236.0 | C ii | 174 | 8727.13 | [CI] |
19 | 3967.46 | [Ne iii]* | 58 | 4769.6 | [Fe iii] | 97 | 5302.86 | [FeXIV] | 136 | 7281.35 | He i | 175 | 8750.47 | H i |
20 | 3968.47 | CaII* | 59 | 4774.74 | [Fe ii] | 98 | 5309.18 | [CaV] | 137 | 7290.42 | [Fe i] | 176 | 8862.78 | H i |
21 | 3970.07 | H![]() | 60 | 4777.88 | [Fe iii] | 99 | 5333.65 | [Fe ii] | 138 | 7291.46 | [Ca ii] | 177 | 8891.88 | [Fe ii] |
22 | 4026.19 | He i | 61 | 4813.9 | [Fe iii] | 100 | 5335.2 | [Fe vi] | 139 | 7318.92 | [O ii] | 178 | 9014.91 | H i* |
23 | 4068.6 | [S ii] | 62 | 4814.55 | [Fe ii] | 101 | 5376.47 | [Fe ii] | 140 | 7323.88 | [Ca ii] | 179 | 9033.45 | [Fe ii] |
24 | 4076.35 | [S ii] | 63 | 4861.36 | Hβ* | 102 | 5411.52 | He ii | 141 | 7329.66 | [O ii] | 180 | 9051.92 | [Fe ii] |
25 | 4101.77 | Hδ* | 64 | 4881.11 | [Fe iii] | 103 | 5412.64 | [Fe ii] | 142 | 7377.83 | [Ni ii] | 181 | 9069.0 | [S iii]* |
26 | 4120.81 | He i | 65 | 4889.63 | [Fe ii] | 104 | 5424.2 | [Fe vi] | 143 | 7388.16 | [Fe ii] | 182 | 9123.6 | [ClII] |
27 | 4177.21 | [Fe ii] | 66 | 4893.4 | [Fe vii] | 105 | 5426.6 | [Fe vi] | 144 | 7411.61 | [Ni ii] | 183 | 9226.6 | [Fe ii] |
28 | 4227.2 | [FeV] | 67 | 4905.35 | [Fe ii] | 106 | 5484.8 | [Fe vi] | 145 | 7452.5 | [Fe ii] | 184 | 9229.02 | H i |
29 | 4243.98 | [Fe ii] | 68 | 4921.93 | He i | 107 | 5517.71 | [Cl iii] | 146 | 7637.52 | [Fe ii] | 185 | 9266.0 | O i |
30 | 4267.0 | C ii | 69 | 4924.5 | [Fe iii] | 108 | 5527.33 | [Fe ii] | 147 | 7686.19 | [Fe ii] | 186 | 9267.54 | [Fe ii] |
31 | 4287.4 | [Fe ii] | 70 | 4930.5 | [Fe iii] | 109 | 5577.34 | [O i] | 148 | 7686.9 | [Fe ii] | 187 | 9399.02 | [Fe ii] |
32 | 4340.49 | Hϒ* | 71 | 4942.5 | [Fe vii] | 110 | 5631.1 | [Fe vi] | 149 | 7751.06 | [Ar iii] | 188 | 9470.93 | [Fe ii]* |
33 | 4358.1 | [Fe ii] | 72 | 4958.91 | [O iii]* | 111 | 5677.0 | [Fe vi] | 150 | 7774.0 | O i | 189 | 9531.1 | [S iii]* |
34 | 4358.37 | [Fe ii] | 73 | 4972.5 | [Fe vi] | 112 | 5720.7 | [Fe vii] | 151 | 7891.8 | [FeXI] | 190 | 9545.97 | H i |
35 | 4359.34 | [Fe ii] | 74 | 4973.39 | [Fe ii] | 113 | 5754.59 | [N ii] | 152 | 7999.85 | [Cr ii] | 191 | 9682.13 | [Fe ii] |
36 | 4363.21 | [O iii] | 75 | 4985.9 | [Fe iii] | 114 | 5876.0 | He i* | 153 | 8125.22 | [Cr ii] | |||
37 | 4413.78 | [Fe ii] | 76 | 5006.84 | [O iii]* | 115 | 5889.95 | Na i* | 154 | 8229.55 | [Cr ii] | |||
38 | 4414.45 | [Fe ii] | 77 | 5015.68 | He i* | 116 | 5895.92 | Na i* | 155 | 8236.77 | He ii |
Note. "#I" corresponds to the index in Table 7, and "Id" is the label of each of the emission lines included in the FLUX_ELINES_LONG extension. "*" designates the emission lines that are detected in at least 5% of the galaxies, with S/N > 3.
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Appendix C: Additional Quality Control
Figure 28 shows the additional information that was used in the visual exploration of the data as part of the quality control process described in Section 4.5. This information was used to identify the presence of strong interacting systems and/or AGNs, which may affect the results from the kinematic analysis (due to the presence of multiple kinematic components) and/or the stellar population exploration (due to strong contamination from a nonstellar source).
Figure 28. An example of the information explored in the quality control process for the galaxy/cube manga-7495-12703, extracted from the analysis presented by López-Cobá et al. (2020). Panels (a) and (b) correspond to the same color images, created using broadband and emission-line images, as shown in Figure 3. Panel (c) comprises the spatially resolved WHAN diagnostic diagram, with each spaxel being color coded using the scheme included in panel (b). Classical BPT diagnostic diagrams, showing the spatially resolved distribution of [O iii]/Hβ as a function of [N ii]/Hα, [S ii]/Hα, and [O i]/Hα are shown in panel (d), using a similar color scheme as panel (c). Panels (e) and (f) show the spatial distributions of the [N ii]/Hα line ratio, in logarithmic scales, and the Hα velocity dispersion, with panel (g) showing the distribution of those parameters one against the other (color coded using the same scheme as panels (c) and (d)). Panels (h), (i), and (j) show the ionized gas velocity map (derived from Hα), the stellar velocity map, and the residual from subtracting the latter from the former, respectively.
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Standard image High-resolution imageAppendix D: List of Oxygen and Nitrogen Abundance Calibrators
Table 15 comprises the list of calibrators extracted from the compilation by Espinosa-Ponce et al. (2022) that have been adopted throughout this exploration. We order the calibrators as they appear in the final catalog for the values reported at the effective radius. We include the correspondences with the calibrators adopted to estimate the oxygen abundance in the central regions, when available. Those calibrators anchored to measurements based on the direct method (usually H ii regions) are labeled as Empirical, while those based on photoionization models are labeled as Theoretical.
Figure 29. Comparison between the oxygen abundances, 12+log(O/H), derived at the effective radius using different calibrators (one for each panel) and listed in the final catalog as a function of the values derived using the Ho (2019) one (adopted as the fiducial one in an arbitrary way). We adopt the same format as described in Figure 19 for the different panels of the figure.
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Standard image High-resolution imageTable 15. List of Oxygen, Nitrogen, and Ionization Parameter Calibrators Adopted to Estimate the Values at the Effective Radius
ID at Re | ID Central Aperture | Emission Lines and Ratios | Calibration Type | Reference |
---|---|---|---|---|
hline OH_Mar13_N2 | OH_N2 | [N ii]λ6548/Hα | Empirical | Marino et al. (2013) |
OH_Mar13_O3N2 | OH_O3N2 | O3N2 | Empirical | Marino et al. (2013) |
OH_T04 | OH_T04 | [N ii]λ6548/Hα, R23 | Empirical | Tremonti et al. (2004) |
OH_Pet04_N2_lin | [N ii]λ6548/Hα | Empirical | Pettini & Pagel (2004) | |
OH_Pet04_N2_poly | [N ii]λ6548/Hα | Empirical | Pettini & Pagel (2004) | |
OH_Pet04_O3N2 | O3N2 | Empirical | Pettini & Pagel (2004) | |
OH_Kew02_N2O2 | [N ii]λ6583/[O ii]λ3727 | Theoretical | Kewley & Dopita (2002) | |
OH_Pil10_ONS | OH_ONS | [N ii]λ6583/Hβ, R2, R3, P, [Sii]/Hα | Empirical | Pilyugin et al. (2010) |
OH_Pil10_ON | [N ii]λ6583/Hβ, R2, R3, [Sii]/Hα | Empirical | Pilyugin et al. (2010) | |
OH_Pil11_NS | [N ii]λ6583/Hβ, R3, [Sii]/Hα | Empirical | Pilyugin & Mattsson (2011) | |
OH_Cur20_RS32 | R3+[Sii]/Hα/Hα | Empirical | Curti et al. (2020) | |
OH_Cur20_R3 | R3 | Empirical | Curti et al. (2020) | |
OH_Cur20_O3O2 | [O iii]λ5007, [O ii]λ3727 + 29 | Empirical | Curti et al. (2020) | |
OH_Cur20_S2 | [Sii]/Hα | Empirical | Curti et al. (2020) | |
OH_Cur20_R2 | R2 | Empirical | Curti et al. (2020) | |
OH_Cur20_N2 | [N ii]λ6583/Hα | Empirical | Curti et al. (2020) | |
OH_Cur20_R23 | R23 | Empirical | Curti et al. (2020) | |
OH_Cur20_O3N2 | R3, [N ii]λ6583/Hα | Empirical | Curti et al. (2020) | |
OH_Cur20_O3S2 | R3/[Sii]/Hα/Hα | Empirical | Curti et al. (2020) | |
OH_KK04 | OH_R23 | R23, [O iii]/[O ii] | Theoretical | Kobulnicky & Kewley (2004) |
OH_Pil16_R | [N ii]λ6583/Hβ, R2, R3 | Empirical | Pilyugin & Grebel (2016) | |
OH_Pil16_S | [N ii]λ6583/Hβ, R3, [Sii]/Hα | Empirical | Pilyugin & Grebel (2016) | |
OH_Ho | R2, R3, [N ii]λ6583/Hβ, [Sii]/Hα/Hα | Empirical | Ho (2019) | |
U_Dors_O32 | [O iii]λ5007, [O ii]λ3727 + 29 | Theoretical | Dors et al. (2011) | |
U_Dors_S2 | [N ii]λ6583/Hα, [Sii]/Hα | Theoretical | Dors et al. (2011) | |
U_Mor16_O32_fs | [O iii]λ5007, [O ii]λ3727 + 29 | Empirical/Theoretical | Morisset et al. (2016) | |
U_Mor16_O32_ts | [O iii]λ5007, [O ii]λ3727 + 29 | Empirical/Theoretical | Morisset et al. (2016) | |
NH_Pil16_R | [N ii]λ6583/Hβ, R2, R3 | Empirical | Pilyugin & Grebel (2016) | |
NO_Pil16_R | [N ii]λ6583/Hβ, R2, R3 | Empirical | Pilyugin & Grebel (2016) | |
NO_Pil16_Ho_R | [N ii]λ6583/Hβ, R2, R3, [Sii]/Hα/Hα | Empirical | Pilyugin & Grebel (2016) & Ho (2019) | |
NO_Pil16_N2_R2 | [N ii]λ6583/Hβ, R2, R3 | Empirical | Pilyugin & Grebel (2016) |
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Figure 29 shows a comparison between the values derived using the complete list of different oxygen abundance calibrators included in the final catalog, as a function of those derived using the Ho (2019) one, after excluding those already discussed in Section 5.3.5 (Figure 14).
Appendix E: Repeated Observations
Table 16 lists the 45 cubes that correspond to repeated observations of the same object. Of them, only seven correspond to galaxies analyzed by pyPipe3D. In one case (manga-9194-12702/manga-9194-12702), the offset between the two pointings is large enough to significantly affect the properties derived from the SDSS imaging data (e.g., Δi ∼ 0.1 mag). For the remaining six cases, we can perform a direct comparison between the data and the parameters derived by our analysis. Despite the fact that the number statistics are very low, this comparison is very relevant, since it is the best one available for estimating the real errors, not only on the physical quantities derived by pyPipe3D, but also in the absolute and relative spectrophotometric calibration of the data, too.
Table 16. Repeated Observations
plate-ifudsgn | Gal. | plate-ifudsgn | Gal. | ||
---|---|---|---|---|---|
10506-12702 | 10504-9102 | y | 8953-9102 | 8479-6101 | n |
11006-6103 | 8480-9102 | n | 8953-9102 | 8480-6102 | n |
8479-9101 | 11006-6103 | n | 8983-3703 | 7495-12704 | y |
8587-3703 | 8479-3702 | n | 9029-12702 | 11941-1901 | n |
8587-3703 | 8480-3703 | n | 9051-3701 | 8479-3702 | n |
8587-3704 | 8479-3701 | n | 9051-3701 | 8480-3703 | n |
8587-3704 | 8480-3702 | n | 9051-3702 | 8479-3701 | n |
8587-6102 | 8479-6104 | n | 9051-3702 | 8480-3702 | n |
8587-6102 | 8480-6101 | n | 9051-3704 | 8479-6101 | n |
8587-6103 | 8479-6101 | n | 9051-3704 | 8480-6102 | n |
8587-6103 | 8480-6102 | n | 9051-6104 | 8479-6102 | n |
8587-6104 | 8479-6102 | n | 9051-6104 | 8480-6103 | n |
8587-6104 | 8480-6103 | n | 9051-9102 | 8479-6104 | n |
8952-12703 | 7495-12705 | y | 9051-9102 | 8480-6101 | n |
8953-3701 | 8479-3701 | n | 9194-12702 | 9193-12702 | y |
8953-3701 | 8480-3702 | n | 9673-12703 | 10142-12701 | n |
8953-3703 | 8479-3702 | n | 9674-12703 | 10143-12701 | n |
8953-3703 | 8480-3703 | n | 9674-9102 | 10143-12705 | n |
8953-6102 | 8479-6104 | n | 9872-1901 | 7443-1901 | y |
8953-6102 | 8480-6101 | n | 9872-3702 | 7443-3701 | y |
8953-6104 | 8479-6102 | n | 9872-3703 | 7443-1902 | y |
8953-6104 | 8480-6103 | n | 9877-12704 | 8479-12701 | n |
9877-12704 | 8480-12705 | n |
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For the comparison between the repeated observations, we selected three parameters to explore the differences in the photometry: (i) the V-band magnitudes; (ii) the B − R colors extracted from the MaNGA data cubes; and (iii) the stellar masses derived from these photometric values. In addition, we selected the same parameters that we adopted to compare between the DR15 and DR17 results (Section 6.2, Figures 19 and 20) and with the DAP results (Section 6.3, Figure 21). Three additional parameters have been included for their relevance: (i) the oxygen abundance (using the Ho 2019 calibrator); (ii) the nitrogen-to-oxygen relative abundance (using the previous oxygen abundance and a calibrator for the nitrogen proposed by Pilyugin & Grebel 2016); and (iii) the ionization parameter (using the calibrator of Morisset et al. 2016), all three of them being measured at the effective radius. Table 17 lists the mean value and the standard deviation of the difference between the values reported in each repeated observation for the selected subset of parameters.
Table 17. Comparison between Repeated Observations
Parameter | Name | Difference |
---|---|---|
Photometric Parameters | ||
V-band mag | V_band_mag | 0.035 ± 0.037 mag |
B − R color | B-R | 0.030 ± 0.015 mag |
M⋆,phot | log_Mass_phot | 0.011 ± 0.026 dex |
Stellar Population Parameters | ||
M⋆ | log_Mass | 0.032 ± 0.105 dex |
ϒ⋆ | ML_avg | 0.052 ± 0.067 dex |
SFR⋆ | log_SFR_ssp | 0.159 ± 0.138 dex |
![]() | Age_LW_Re_fit | −0.054 ± 0.061 dex |
![]() | ZH_LW_Re_fit | 0.044 ± 0.026 dex |
AV,⋆ | Av_ssp_Re | 0.098 ± 0.068 dex |
σ⋆,cen | vel_disp_ssp_cen | −17.5 ± 22.9 km s−1 |
![]() | vel_ssp_2 | −27.9 ± 59.9 km s−1 |
![]() | Lambda_Re | 0.134 ± 0.166 |
Emission-line Parameters | ||
Hα flux | F_Ha_cen | −0.050 ± 0.067 dex |
EW(Hα) | EW_Ha_cen | −0.065 ± 0.128 dex |
Hα/Hβ | Ha_Hb_cen | 0.006 ± 0.032 dex |
[OIII]/Hβ | log_OIII_Hb_cen | −0.036 ± 0.083 dex |
[NII]/Hα | log_NII_Ha_cen | 0.020 ± 0.053 dex |
[SII]/Hα | log_SII_Ha_cen | −0.016 ± 0.040 dex |
AV,gas | Av_gas_Re | −0.169 ± 0.237 mag |
SFRHα | log_SFR_Ha | −0.041 ± 0.097 dex |
Mgas | log_Mass_gas | −0.003 ± 0.008 dex |
12+log(O/H) | OH_Ho_Re_fit | 0.018 ± 0.024 dex |
log(N/O) | NO_Pil16_Ho_R_Re_fit | −0.004 ± 0.022 dex |
log(U) | U_Mor16_O23_fs_Re_fit | −0.092 ± 0.135 dex |
Stellar Indices | ||
Fe5270 | Fe5270_Re_fit | -0.035 ± 0.356 Å |
Fe5335 | Fe5335_Re_fit | 0.352 ± 0.510 Å |
Mgb | Mgb_Re_fit | 0.115 ± 0.350 Å |
Hδ | Hd_Re_fit | −0.038 ± 0.362 Å |
Hβ | Hb_Re_fit | 0.048 ± 0.247 Å |
D4000 | D4000_Re_fit1 | −0.018 ± 0.020 |
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The agreement between the photometric parameters is very good, with the mean offsets and the standard deviations around them being of the order of a few percent. This is the case for both the V-band magnitude and the B − R color. If these values were representative of the full data set (which we cannot assure, due to the very low number statistics), they would indicate an extremely accurate and precise absolute and relative (blue-to-red) spectrophotometric calibration. These results agree with the expectations and earlier reports of the quality of the MaNGA spectrophotometric calibration (e.g., Yan et al. 2016a, 2016b). These photometric errors are propagated through all the derived quantities, irrespective of the adopted analysis. For instance, in the case of the stellar mass derived using purely photometric information, we found an offset of just ∼0.01 dex, but a scatter of ∼0.03 dex.
As expected, the differences in the stellar properties derived from the decomposition of the stellar population using pyPipe3D are larger than those found for the purely photometric quantities. The standard deviation of the difference for the stellar mass is of the order of 10%, which is the result of the propagation of the photometric error (∼3% in M⋆,phot) and the uncertainties in the derivation of the mass-to-light ratio (∼7%). Thus, the uncertainty introduced by the methodology is three times larger than the one introduced by just the photometric errors. Again, this result is in agreement with the expectations, based on the comparison between the DR15 and DR17 results discussed in Section 6.2. As a consequence, the errors in the determination of the SFR based on the stellar population analysis are larger than the ones reported for M⋆, since this estimation relies on the same analysis, but requires the estimation of the fraction of the youngest stellar populations (which is more imprecise). The differences in the estimation of the main properties of the stellar population (,
, and AV,⋆), are of the order of a few percent, ranging between ∼3% and ∼10%. These values agree with the expectations based on the simulations for our code (Sánchez et al. 2016b; Lacerda et al. 2022). Finally, the differences in the kinematics parameters for the stellar populations are of the same order as the ones found when comparing the analyses for the DR15 and DR17 data sets. Therefore, we consider that these errors are most probably associated with the ability to extract the corresponding kinematics information for these kinds of data (i.e., S/N and spectral resolution) using the current methodology.
Once more, the emission-line properties present differences that, in general, are larger than expected from the pure propagation of the photometric errors. In general, extensive/integrated quantities, like the Hα flux or the integrated SFRHα , present larger differences (∼7%–10%) than intensive/relative ones, like the different line ratios (∼3%–8%). Significant deviations from this picture are the results for EW(Hα) and AV,gas, which present large average differences and scatters (∼10%–13%). Finally, the differences in the estimations of the oxygen and nitrogen-to-oxygen abundances are much lower than for the rest of the explored parameters, most probably due to the facts that they are derived based on a radial gradient analysis (Section 5.3) and that both parameters present well-defined radial gradients. On the other hand, the ionization parameter presents the largest differences among the different explored parameters for the ionized gas (∼10%–14%).
For the stellar indices, we report differences of the order of those found when comparing the results between our analysis and the DAP (Section 6.3, Figure 21), although they are slightly lower. For instance, for the Hβ and Fe5270 indices, we report a scatter of ∼0.25–0.36 Å, with a very small average difference (∼0.05 Å), while the comparison with the DAP results in a scatter of ∼0.43–0.55 Å and a clear offset, at least for Hβ (∼0.21 Å). However, for other indices, like Fe5335, we find a larger offset in the current comparison (∼0.35 Å versus ∼0.13 Å) and a similar scatter (∼0.5–0.6 Å). Due to the limited number of objects with repeated observations, it is difficult to asses whether the reported differences are really larger than the ones found when comparing with the DAP.
Despite the low number statistics, we consider this comparison to be one of the best gauges of the uncertainties of the derived parameters. In general, the results agree with the expectations and with previous comparisons, and they highlight the limitations and real uncertainties of the adopted methodology and current data set. We recommend that these uncertainties are considered in any further discussion based on the current analysis.
Appendix F: Properties Included in the Catalog
Table 18 lists the integrated, characteristic, and aperture-extracted properties included in the catalog of properties, derived by the analysis described throughout this article. For each property, the table includes the column of the FITs-table extension in which it is stored (Col. No.), the adopted name for the parameter, its unit, and a short description of the delivered quantity.
Table 18. Integrated and Characteristic Parameters Delivered for Each Analyzed Data Cube
Col. No. | Parameter Name | Units | Description |
---|---|---|---|
0 | name | ⋯ | manga-plate-ifudsgn unique name |
1 | plate | ⋯ | Plate ID of the MaNGA cube |
2 | ifudsgn | ⋯ | IFU bundle ID of the MaNGA cube |
3 | plateifu | ⋯ | Code formed by the plate and ifu names |
4 | mangaid | ⋯ | MaNGA ID |
5 | objra | degrees | R.A. of the object |
6 | objdec | degrees | Decl. of the object |
7 | log_SFR_Hα | log(M⊙ yr−1) | Integrated SFR derived from the integrated Hα luminosity |
8 | FOV | ⋯ | Ratio between the diagonal radius of the cube and effective radius |
9 | Re_kpc | kpc | Effective radius in kiloparsecs |
10 | e_log_Mass | log(M⊙) | Error of the integrated stellar mass in logarithmic scale |
11 | e_log_SFR_Hα | log(M⊙ yr−1) | Error of the integrated SFR derived from the Hα luminosity |
12 | log_Mass | log(M⊙) | Integrated stellar mass in units of the solar mass in logarithmic scale |
13 | log_SFR_ssp | log(M⊙ yr−1) | Integrated SFR derived from the SSP analysis t < 32 Myr |
14 | log_NII_Ha_cen | ⋯ | Logarithm of the [N ii]6583/Hα line ratio in the central aperture |
15 | e_log_NII_Ha_cen | ⋯ | Error in the logarithm of the [N ii]6583/Hα line ratio |
16 | log_OIII_Hb_cen | ⋯ | Logarithm of the [O iii]5007/Hβ line ratio in the central aperture |
17 | e_log_OIII_Hb_cen | ⋯ | Error in the logarithm of the [O iii]5007/Hβ line ratio |
18 | log_SII_Ha_cen | ⋯ | Logarithm of the [S ii]6717+6731/Hα line ratio in the central aperture |
19 | e_log_SII_Ha_cen | ⋯ | Error in the logarithm of the [S ii]6717/Hα line ratio |
20 | log_OII_Hb_cen | ⋯ | Logarithm of the [O ii]3727/Hβ line ratio in the central aperture |
21 | e_log_OII_Hb_cen | ⋯ | Error in the logarithm of the [O ii]3727/Hβ line ratio |
22 | EW_Ha_cen | Å | Equivalent width of Hα in the central aperture |
23 | e_EW_Ha_cen | Å | Error of the equivalent width of Hα in the central aperture |
24 | ZH_LW_Re_fit | dex | LW metallicity of the stellar population at the effective radius, normalized to the solar value, in logarithmic scales |
25 | e_ZH_LW_Re_fit | dex | Error in the LW metallicity of the stellar population |
26 | alpha_ZH_LW_Re_fit | dex/Re | Slope of the gradient of the LW metallicity of the stellar population |
27 | e_alpha_ZH_LW_Re_fit | dex/Re | Error of the slope of the gradient of the LW log-metallicity |
28 | ZH_MW_Re_fit | dex | MW metallicity of the stellar population at the effective radius, normalized to the solar value, in logarithmic scales |
29 | e_ZH_MW_Re_fit | dex | Error in the MW metallicity of the stellar population |
30 | alpha_ZH_MW_Re_fit | dex/Re | Slope of the gradient of the MW log-metallicity of the stellar population |
31 | e_alpha_ZH_MW_Re_fit | dex/Re | Error of the slope of the gradient of the MW log-metallicity |
32 | Age_LW_Re_fit | log(yr) | LW age of the stellar population in logarithmic scale |
33 | e_Age_LW_Re_fit | log(yr) | Error in the LW age of the stellar population |
34 | alpha_Age_LW_Re_fit | log(yr)/Re | Slope of the gradient of the LW log-age of the stellar population |
35 | e_alpha_Age_LW_Re_fit | log(yr)/Re | Error of the slope of the gradient of the LW log-age of the stellar population |
36 | Age_MW_Re_fit | log(yr) | MW age of the stellar population in logarithm |
37 | e_Age_MW_Re_fit | log(yr) | Error in the MW age of the stellar population |
38 | alpha_Age_MW_Re_fit | log(yr)/Re | Slope of the gradient of the MW log-age of the stellar population |
39 | e_alpha_Age_MW_Re_fit | log(yr)/Re | Error of the slope of the gradient of the MW log-age of the stellar population |
40 | Re_arc | arcseconds | Adopted effective radius in arcseconds |
41 | DL | Mpc | Adopted luminosity distance |
42 | DA | Mpc | Adopted angular diameter distance |
43 | PA | degrees | Adopted position angle in degrees |
44 | ellip | ⋯ | Adopted ellipticity |
45 | log_Mass_gas | log(M⊙) | Integrated gas mass in units of the solar mass in logarithmic scale |
46 | vel_sigma_Re | ⋯ | Velocity/dispersion ratio for the stellar populations within 1.5 Re |
47 | e_vel_sigma_Re | ⋯ | Error in the velocity/dispersion ratio for the stellar population |
48 | log_SFR_SF | log(M⊙ yr−1) | Integrated SFR using only the spaxels compatible with SF |
49 | log_SFR_D_C | log(M⊙ yr−1) | Integrated SFR diffuse corrected |
50 | OH_O3N2_cen | dex | Oxygen abundance using the calibrator O3N2 at the central aperture |
51 | e_OH_O3N2_cen | dex | Error in the oxygen abundance using the calibrator O3N2 at the central aperture |
52 | OH_N2_cen | dex | Oxygen abundance using the calibrator N2 at the central aperture |
53 | e_OH_N2_cen | dex | Error in the oxygen abundance using the calibrator N2 at the central aperture |
54 | OH_ONS_cen | dex | Oxygen abundance using the calibrator ONS at the central aperture |
55 | e_OH_ONS_cen | dex | Error in the oxygen abundance using the calibrator ONS at the central aperture |
56 | OH_R23_cen | dex | Oxygen abundance using the calibrator R23 at the central aperture |
57 | e_OH_R23_cen | dex | Error in the oxygen abundance using the calibrator R23 at the central aperture |
58 | OH_pyqz_cen | dex | Oxygen abundance using the calibrator pyqz at the central aperture |
59 | e_OH_pyqz_cen | dex | Error in the oxygen abundance using the calibrator pyqz at the central aperture |
60 | OH_t2_cen | dex | Oxygen abundance using the calibrator t2 at the central aperture |
61 | e_OH_t2_cen | dex | Error in the oxygen abundance using the calibrator t2 at the central aperture |
62 | OH_M08_cen | dex | Oxygen abundance using the calibrator M08 at the central aperture |
63 | e_OH_M08_cen | dex | Error in the oxygen abundance using the calibrator M08 at the central aperture |
64 | OH_T04_cen | dex | Oxygen abundance using the calibrator T04 at the central aperture |
65 | e_OH_T04_cen | dex | Error in the oxygen abundance using the calibrator T04 at the central aperture |
66 | OH_dop_cen | dex | Oxygen abundance using the calibrator dop at the central aperture |
67 | e_OH_dop_cen | dex | Error in the oxygen abundance using the calibrator dop at the central aperture |
68 | OH_O3N2_EPM09_cen | dex | Oxygen abundance using the calibrator O3N2_EPM09 at the central aperture |
69 | e_OH_O3N2_EPM09_cen | dex | Error in the oxygen abundance using the calibrator O3N2_EPM09 at the central aperture |
70 | log_OI_Ha_cen | dex | Logarithm of the [O i]6301/Hα within a central aperture |
71 | e_log_OI_Ha_cen | dex | Error in the logarithm of the [O i]6301/Hα within a central aperture |
72 | Ha_Hb_cen | ⋯ | Ratio between the flux of Haα and Hβ within a central aperture |
73 | e_Ha_Hb_cen | ⋯ | Error of Ha_Hb_cen |
74 | log_NII_Ha_Re | dex | Logarithm of the [N ii]6583/Hα line ratio at 1 Re |
75 | e_log_NII_Ha_Re | dex | Error in the logarithm of the [N ii]6583/Hα ratio at 1 Re |
76 | log_OIII_Hb_Re | dex | Logarithm of the [O iii]5007/Hβ line ratio at 1 Re |
77 | e_log_OIII_Hb_Re | dex | Error in the logarithm of the [O iii]5007/Hβ ratio at 1 Re |
78 | log_SII_Ha_Re | dex | Logarithm of the [S ii]6717+6731/Hα ratio at 1 Re |
79 | e_log_SII_Ha_Re | dex | Error in the logarithm of the [S ii]6717/Hα ratio at 1 Re |
80 | log_OII_Hb_Re | dex | Logarithm of the [O ii]3727/Hβ ratio at 1 Re |
81 | e_log_OII_Hb_Re | dex | Error in the logarithm of the [O ii]3727/Hβ at 1 Re |
82 | log_OI_Ha_Re | dex | Logarithm of the [O i]6301/Hα ratio at 1 Re |
83 | e_log_OI_Ha_Re | dex | Error in the logarithm of the [O i]6301/Hα ratio at 1 Re |
84 | EW_Ha_Re | Å | EW of Hα at 1 Re |
85 | e_EW_Ha_Re | Å | Error of the EW of Hα at 1 Re |
86 | Ha_Hb_Re | ⋯ | Ratio between the flux of Hα and Hβ within at 1 Re |
87 | e_Ha_Hb_Re | ⋯ | Error of Ha_Hb_Re |
88 | log_NII_Ha_ALL | dex | Logarithm of the [N ii]6583/Hα line ratio within the entire FOV |
89 | e_log_NII_Ha_ALL | dex | Error in the logarithm of the [N ii]6583/α ratio within the entire FOV |
90 | log_OIII_Hb_ALL | dex | Logarithm of the [O iii]5007/Hβ line ratio within the FOV |
91 | e_log_OIII_Hb_ALL | dex | Error in the logarithm of the [O iii]5007/Hβ ratio within the FOV |
92 | log_SII_Ha_ALL | dex | Logarithm of the [S ii]6717+6731/Hα ratio within the entire FOV |
93 | e_log_SII_Ha_ALL | dex | Error in the logarithm of the [S ii]6717/Hα ratio within the FOV |
94 | log_OII_Hb_ALL | dex | Logarithm of the [O ii]3727/Hβ ratio within the entire FOV |
95 | e_log_OII_Hb_ALL | dex | Error in the logarithm of the [O ii]3727/Hβ ratio within the FOV |
96 | log_OI_Ha_ALL | dex | Logarithm of the [O i]6301/Hα ratio within the entire FOV |
97 | e_log_OI_Ha_ALL | dex | Error in the logarithm of the [O i]6301/Hβ ratio within the FOV |
98 | EW_Ha_ALL | Å | EW of Hα within the entire FOV |
99 | e_EW_Ha_ALL | Å | Error of the EW of Hα within the FOV |
100 | Ha_Hb_ALL | ⋯ | Ratio between the flux of Hα and Hβ within the entire FOV |
101 | Sigma_Mass_cen | log(M⊙ pc−2) | Stellar-mass surface density in the central aperture |
102 | e_Sigma_Mass_cen | log(M⊙ pc−2) | Error in the stellar-mass surface density in the central aperture |
103 | Sigma_Mass_Re | log(M⊙ pc−2) | Stellar-mass surface density at 1 Re |
104 | e_Sigma_Mass_Re | log(M⊙ pc−2) | Error in the stellar-mass surface density at 1 Re |
105 | Sigma_Mass_ALL | log(M⊙ pc−2) | Average stellar-mass surface density within the entire FOV |
106 | e_Sigma_Mass_ALL | log(M⊙ pc−2) | Error in the average stellar-mass surface density within the FOV |
107 | T30 | Gyr | Lookback time at which the galaxy has formed 30% of its stellar mass |
108 | ZH_T30 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T30 time |
109 | ZH_Re_T30 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T30 time |
110 | a_ZH_T30 | dex/Re | Slope of the ZH gradient at T30 time |
111 | T40 | Gyr | Lookback time at which the galaxy has formed 40% of its stellar mass |
112 | ZH_T40 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T40 time |
113 | ZH_Re_T40 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T40 time |
114 | a_ZH_T40 | dex/Re | Slope of the ZH gradient at T40 time |
115 | T50 | Gyr | Lookback time at which the galaxy has formed 50% of its stellar mass |
116 | ZH_T50 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T50 time |
117 | ZH_Re_T50 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T50 time |
118 | a_ZH_T50 | dex/Re | Slope of the ZH gradient at T50 time |
119 | T60 | Gyr | Lookback time at which the galaxy has formed 60% of its stellar mass |
120 | ZH_T60 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T60 time |
121 | ZH_Re_T60 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T60 time |
122 | a_ZH_T60 | dex/Re | Slope of the ZH gradient at T60 time |
123 | T70 | Gyr | Lookback time at which the galaxy has formed 70% of its stellar mass |
124 | ZH_T70 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T70 time |
125 | ZH_Re_T70 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T70 time |
126 | a_ZH_T70 | dex/Re | Slope of the ZH gradient at T70 time |
127 | T80 | Gyr | Lookback time at which the galaxy has formed 80% of its stellar mass |
128 | ZH_T80 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T80 time |
129 | ZH_Re_T80 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T80 time |
130 | a_ZH_T80 | dex/Re | Slope of the ZH gradient at T80 time |
131 | T90 | Gyr | Lookback time at which the galaxy has formed 90% of its stellar mass |
132 | ZH_T90 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T90 time |
133 | ZH_Re_T90 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T90 time |
134 | a_ZH_T90 | dex/Re | Slope of the ZH gradient at T90 time |
135 | T95 | Gyr | Lookback time at which the galaxy has formed 95% of its stellar mass |
136 | ZH_T95 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T95 time |
137 | ZH_Re_T95 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T95 time |
138 | a_ZH_T95 | dex/Re | Slope of the ZH gradient at T95 time |
139 | T99 | Gyr | Lookback time at which the galaxy has formed 99% of its stellar mass |
140 | ZH_T99 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at T99 time |
141 | ZH_Re_T99 | dex | Stellar metallicity, normalized by the solar value, in logarithmic scale at Re at T99 time |
142 | a_ZH_T99 | dex/Re | Slope of the ZH gradient at T99 time |
143 | log_Mass_gas_Av_gas_OH | log(M⊙) | Integrated gas mass using the calibrator log_Mass_gas_Av_gas_OH, in logarithmic scale |
144 | log_Mass_gas_Av_ssp_OH | log(M⊙) | Integrated gas mass using the calibrator log_Mass_gas_Av_ssp_OH, in logarithmic scale |
145 | vel_ssp_2 | km s−1 | Stellar velocity at 2 Re |
146 | e_vel_ssp_2 | km s−1 | Error in vel_ssp_2 |
147 | vel_Ha_2 | km s−1 | Hα velocity at 2 Re |
148 | e_vel_Ha_2 | km s−1 | Error in vel_Ha_2 |
149 | vel_ssp_1 | km s−1 | Stellar velocity at 1 Re |
150 | e_vel_ssp_1 | km s−1 | Error of vel_ssp_1 |
151 | vel_Ha_1 | km s−1 | Hα velocity at 1 Re |
152 | e_vel_Ha_1 | km s−1 | Error of e_vel_Ha_1 |
153 | log_SFR_ssp_100Myr | log(M⊙ yr−1) | Integrated SFR derived from the SSP analysis for t < 100 Myr |
154 | log_SFR_ssp_10Myr | log(M⊙ yr−1) | Integrated SFR derived from the SSP analysis for t < 10 Myr |
155 | vel_disp_Ha_cen | km s−1 | Hα velocity dispersion at the central aperture |
156 | vel_disp_ssp_cen | km s−1 | Stellar velocity dispersion at the central aperture |
157 | vel_disp_Ha_1Re | km s−1 | Hα velocity dispersion at 1 Re |
158 | vel_disp_ssp_1Re | km s−1 | Stellar velocity at 1 Re |
159 | log_Mass_in_Re | log(M⊙) | Integrated stellar mass within 1 optical Re |
160 | ML_int | M⊙/L⊙ | V-band mass-to-light ratio from integrated quantities |
161 | ML_avg | M⊙/L⊙ | V-band mass-to-light ratio averaged across the FOV |
162 | F_Ha_cen | 10−16 erg s−1 cm−2 | Flux intensity of Hα in the central aperture |
163 | e_F_Ha_cen | 10−16 erg s−1 cm−2 | Error of F_Ha_cen |
164 | R50_kpc_V | kpc | Radius at which half of the light in the V-band within the FOV is integrated |
165 | e_R50_kpc_V | kpc | Error of R50_kpc_V |
166 | R50_kpc_Mass | kpc | Radius at which half of the stellar mass within the FOV is integrated |
167 | e_R50_kpc_Mass | kpc | Error of R50_kpc_Mass |
168 | log_Mass_corr_in_R50_V | log(M⊙) | Integrated stellar mass within R50_kpc_V |
169 | e_log_Mass_corr_in_R50_V | log(M⊙) | Error of log_Mass_corr_in_R50_V |
170 | log_Mass_gas_Av_gas_log_log | log(M⊙) | Molecular gas mass derived from the dust extinction using the log-log calibration |
171 | Av_gas_Re | mag | Dust extinction in the V band derived from the Hα/Hβ line ratio at 1 Re |
172 | e_Av_gas_Re | mag | Error of Av_gas_Re |
173 | Av_ssp_Re | mag | Dust extinction in the V band derived from the analysis of the stellar population |
174 | e_Av_ssp_Re | mag | Error of the Av_ssp_Re |
175 | Lambda_Re | ⋯ | Specific angular momentum (λ-parameter) for the stellar populations within 1 Re |
176 | e_Lambda_Re | ⋯ | Error of the Lambda_Re parameter |
177 | nsa_redshift | ⋯ | ⋯ |
178 | nsa_mstar | log(M⊙) | Stellar mass derived by the NSA collaboration |
179 | nsa_inclination | deg | Inclination derived by the NSA collaboration |
180 | flux_[OII]3726.03_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O ii]3726.03 at 1 Re |
181 | e_flux_[OII]3726.03_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O ii]3726.03 at 1 Re |
182 | flux_[OII]3726.03_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O ii]3726.03 at 1 Re |
183 | e_flux_[OII]3726.03_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O ii]3726.03 at 1 Re |
184 | flux_[OII]3728.82_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O ii]3728.82 at 1 Re |
185 | e_flux_[OII]3728.82_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O ii]3728.82 at 1 Re |
186 | flux_[OII]3728.82_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O ii]3728.82 at 1 Re |
187 | e_flux_[OII]3728.82_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O ii]3728.82 at 1 Re |
188 | flux_HI3734.37_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HI3734.37 at 1 Re |
189 | e_flux_HI3734.37_Re_fit | 10−16erg s−1 cm−2 | Error in the flux intensity of HI3734.37 at 1 Re |
190 | flux_HI3734.37_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of HI3734.37 at 1 Re |
191 | e_flux_HI3734.37_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HI3734.37 at 1 Re |
192 | flux_HI3797.9_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HI3797.9 at 1 Re |
193 | e_flux_HI3797.9_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HI3797.9 at 1 Re |
194 | flux_HI3797.9_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of HI3797.9 at 1 Re |
195 | e_flux_HI3797.9_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HI3797.9 at 1 Re |
196 | flux_HeI3888.65_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI3888.65 at 1 Re |
197 | e_flux_HeI3888.65_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI3888.65 at 1 Re |
198 | flux_HeI3888.65_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI3888.65 at 1 Re |
199 | e_flux_HeI3888.65_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI3888.65 at 1 Re |
200 | flux_HI3889.05_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HI3889.05 at 1 Re |
201 | e_flux_HI3889.05_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HI3889.05 at 1 Re |
202 | flux_HI3889.05_alpha_fit | 10−16erg s−1 cm−2 | Flux intensity of HI3889.05 at 1 Re |
203 | e_flux_HI3889.05_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HI3889.05 at 1 Re |
204 | flux_HeI3964.73_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI3964.73 at 1 Re |
205 | e_flux_HeI3964.73_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI3964.73 at 1 Re |
206 | flux_HeI3964.73_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI3964.73 at 1 Re |
207 | e_flux_HeI3964.73_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI3964.73 at 1 Re |
208 | flux_[NeIII]3967.46_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [Ne iii]3967.46 at 1 Re |
209 | e_flux_[NeIII]3967.46_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [Ne iii]3967.46 at 1 Re |
210 | flux_[NeIII]3967.46_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [Ne iii]3967.46 at 1 Re |
211 | e_flux_[NeIII]3967.46_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [Ne iii]3967.46 at 1 Re |
212 | flux_CaII3968.47_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of CaII3968.47 at 1 Re |
213 | e_flux_CaII3968.47_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of CaII3968.47 at 1 Re |
214 | flux_CaII3968.47_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of CaII3968.47 at 1 Re |
215 | e_flux_CaII3968.47_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of CaII3968.47 at 1 Re |
216 | flux_Hepsilon3970.07_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of H![]() |
217 | e_flux_Hepsilon3970.07_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of H![]() |
218 | flux_Hepsilon3970.07_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of H![]() |
219 | e_flux_Hepsilon3970.07_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of H![]() |
220 | flux_Hdelta4101.77_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of Hδ4101.77 at 1 Re |
221 | e_flux_Hdelta4101.77_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hδ4101.77 at 1 Re |
222 | flux_Hdelta4101.77_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of Hδ4101.77 at 1 Re |
223 | e_flux_Hdelta4101.77_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hδ4101.77 at 1 Re |
224 | flux_Hgamma4340.49_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of Hγ4340.49 at 1 Re |
225 | e_flux_Hgamma4340.49_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hγ4340.49 at 1 Re |
226 | flux_Hgamma4340.49_alpha_fit | 10−16erg s−1 cm−2 | Flux intensity of Hγ4340.49 at 1 Re |
227 | e_flux_Hgamma4340.49_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hγ4340.49 at 1 Re |
228 | flux_Hbeta4861.36_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of Hβ4861.36 at 1 Re |
229 | e_flux_Hbeta4861.36_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hβ4861.36 at 1 Re |
230 | flux_Hbeta4861.36_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of Hβ4861.36 at 1 Re |
231 | e_flux_Hbeta4861.36_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hβ4861.36 at 1 Re |
232 | flux_[OIII]4958.91_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O iii]4958.91 at 1 Re |
233 | e_flux_[OIII]4958.91_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O iii]4958.91 at 1 Re |
234 | flux_[OIII]4958.91_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O iii]4958.91 at 1 Re |
235 | e_flux_[OIII]4958.91_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O iii]4958.91 at 1 Re |
236 | flux_[OIII]5006.84_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O iii]5006.84 at 1 Re |
237 | e_flux_[OIII]5006.84_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O iii]5006.84 at 1 Re |
238 | flux_[OIII]5006.84_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O iii]5006.84 at 1 Re |
239 | e_flux_[OIII]5006.84_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O iii]5006.84 at 1 Re |
240 | flux_HeI5015.68_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI5015.68 at 1 Re |
241 | e_flux_HeI5015.68_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI5015.68 at 1 Re |
242 | flux_HeI5015.68_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI5015.68 at 1 Re |
243 | e_flux_HeI5015.68_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI5015.68 at 1 Re |
244 | flux_[NI]5197.9_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N i]5197.9 at 1 Re |
245 | e_flux_[NI]5197.9_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N i]5197.9 at 1 Re |
246 | flux_[NI]5197.9_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N i]5197.9 at 1 Re |
247 | e_flux_[NI]5197.9_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N i]5197.9 at 1 Re |
248 | flux_[NI]5200.26_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N i]5200.26 at 1 Re |
249 | e_flux_[NI]5200.26_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N i]5200.26 at 1 Re |
250 | flux_[NI]5200.26_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N i]5200.26 at 1 Re |
251 | e_flux_[NI]5200.26_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N i]5200.26 at 1 Re |
252 | flux_HeI5876.0_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI5876.0 at 1 Re |
253 | e_flux_HeI5876.0_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI5876.0 at 1 Re |
254 | flux_HeI5876.0_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of HeI5876.0 at 1 Re |
255 | e_flux_HeI5876.0_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HeI5876.0 at 1 Re |
256 | flux_NaI5889.95_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of NaI5889.95 at 1 Re |
257 | e_flux_NaI5889.95_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of NaI5889.95 at 1 Re |
258 | flux_NaI5889.95_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of NaI5889.95 at 1 Re |
259 | e_flux_NaI5889.95_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of NaI5889.95 at 1 Re |
260 | flux_NaI5895.92_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of NaI5895.92 at 1 Re |
261 | e_flux_NaI5895.92_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of NaI5895.92 at 1 Re |
262 | flux_NaI5895.92_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of NaI5895.92 at 1 Re |
263 | e_flux_NaI5895.92_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of NaI5895.92 at 1 Re |
264 | flux_[OI]6300.3_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O i]6300.3 at 1 Re |
265 | e_flux_[OI]6300.3_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O i]6300.3 at 1 Re |
266 | flux_[OI]6300.3_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [O i]6300.3 at 1 Re |
267 | e_flux_[OI]6300.3_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [O i]6300.3 at 1 Re |
268 | flux_[NII]6548.05_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N ii]6548.05 at 1 Re |
269 | e_flux_[NII]6548.05_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N ii]6548.05 at 1 Re |
270 | flux_[NII]6548.05_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N ii]6548.05 at 1 Re |
271 | e_flux_[NII]6548.05_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N ii]6548.05 at 1 Re |
272 | flux_Halpha6562.85_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of Hα6562.85 at 1 Re |
273 | e_flux_Halpha6562.85_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hα6562.85 at 1 Re |
274 | flux_Halpha6562.85_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of Hα6562.85 at 1 Re |
275 | e_flux_Halpha6562.85_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of Hα6562.85 at 1 Re |
276 | flux_[NII]6583.45_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N ii]6583.45 at 1 Re |
277 | e_flux_[NII]6583.45_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N ii]6583.45 at 1 Re |
278 | flux_[NII]6583.45_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [N ii]6583.45 at 1 Re |
279 | e_flux_[NII]6583.45_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [N ii]6583.45 at 1 Re |
280 | flux_[SII]6716.44_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S ii]6716.44 at 1 Re |
281 | e_flux_[SII]6716.44_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S ii]6716.44 at 1 Re |
282 | flux_[SII]6716.44_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S ii]6716.44 at 1 Re |
283 | e_flux_[SII]6716.44_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S ii]6716.44 at 1 Re |
284 | flux_[SII]6730.82_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S ii]6730.82 at 1 Re |
285 | e_flux_[SII]6730.82_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S ii]6730.82 at 1 Re |
286 | flux_[SII]6730.82_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S ii]6730.82 at 1 Re |
287 | e_flux_[SII]6730.82_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S ii]6730.82 at 1 Re |
288 | flux_[ArIII]7135.8_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [Ar iii]7135.8 at 1 Re |
289 | e_flux_[ArIII]7135.8_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [Ar iii]7135.8 at 1 Re |
290 | flux_[ArIII]7135.8_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [Ar iii]7135.8 at 1 Re |
291 | e_flux_[ArIII]7135.8_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [Ar iii]7135.8 at 1 Re |
292 | flux_HI9014.91_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of HI9014.91 at 1 Re |
293 | e_flux_HI9014.91_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HI9014.91 at 1 Re |
294 | flux_HI9014.91_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of HI9014.91 at 1 Re |
295 | e_flux_HI9014.91_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of HI9014.91 at 1 Re |
296 | flux_[SIII]9069.0_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S iii]9069.0 at 1 Re |
297 | e_flux_[SIII]9069.0_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S iii]9069.0 at 1 Re |
298 | flux_[SIII]9069.0_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S iii]9069.0 at 1 Re |
299 | e_flux_[SIII]9069.0_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S iii]9069.0 at 1 Re |
300 | flux_[FeII]9470.93_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [Fe ii]9470.93 at 1 Re |
301 | e_flux_[FeII]9470.93_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [Fe ii]9470.93 at 1 Re |
302 | flux_[FeII]9470.93_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [Fe ii]9470.93 at 1 Re |
303 | e_flux_[FeII]9470.93_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [Fe ii]9470.93 at 1 Re |
304 | flux_[SIII]9531.1_Re_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S iii]9531.1 at 1 Re |
305 | e_flux_[SIII]9531.1_Re_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S iii]9531.1 at 1 Re |
306 | flux_[SIII]9531.1_alpha_fit | 10−16 erg s−1 cm−2 | Flux intensity of [S iii]9531.1 at 1 Re |
307 | e_flux_[SIII]9531.1_alpha_fit | 10−16 erg s−1 cm−2 | Error in the flux intensity of [S iii]9531.1 at 1 Re |
308 | OH_Mar13_N2_Re_fit a | dex | Oxygen abundance using the calibrator Mar13_N2 at 1 Re |
309 | e_OH_Mar13_N2_Re_fit | dex | Error in the oxygen abundance using the calibrator Mar13_N2 at 1 Re |
310 | OH_Mar13_N2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Mar13_N2 |
311 | e_OH_Mar13_N2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Mar13_N2 |
312 | OH_Mar13_O3N2_Re_fit | dex | Oxygen abundance using the calibrator Mar13_O3N2 at 1 Re |
313 | e_OH_Mar13_O3N2_Re_fit | dex | Error in the oxygen abundance using the calibrator Mar13_O3N2 at 1 Re |
314 | OH_Mar13_O3N2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Mar13_O3N2 |
315 | e_OH_Mar13_O3N2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Mar13_O3N2 |
316 | OH_T04_Re_fit | dex | Oxygen abundance using the calibrator T04 at 1 Re |
317 | e_OH_T04_Re_fit | dex | Error in the oxygen abundance using the calibrator T04 at 1 Re |
318 | OH_T04_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator T04 |
319 | e_OH_T04_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator T04 |
320 | OH_Pet04_N2_lin_Re_fit | dex | Oxygen abundance using the calibrator Pet04_N2_lin at 1 Re |
321 | e_OH_Pet04_N2_lin_Re_fit | dex | Error in the oxygen abundance using the calibrator Pet04_N2_lin at 1 Re |
322 | OH_Pet04_N2_lin_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pet04_N2_lin |
323 | e_OH_Pet04_N2_lin_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pet04_N2_lin |
324 | OH_Pet04_N2_poly_Re_fit | dex | Oxygen abundance using the calibrator Pet04_N2_poly at 1 Re |
325 | e_OH_Pet04_N2_poly_Re_fit | dex | Error in the oxygen abundance using the calibrator Pet04_N2_poly at 1 Re |
326 | OH_Pet04_N2_poly_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pet04_N2_poly |
327 | e_OH_Pet04_N2_poly_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pet04_N2_poly |
328 | OH_Pet04_O3N2_Re_fit | dex | Oxygen abundance using the calibrator Pet04_O3N2 at 1 Re |
329 | e_OH_Pet04_O3N2_Re_fit | dex | Error in the oxygen abundance using the calibrator Pet04_O3N2 at 1 Re |
330 | OH_Pet04_O3N2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pet04_O3N2 |
331 | e_OH_Pet04_O3N2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pet04_O3N2 |
332 | OH_Kew02_N2O2_Re_fit | dex | Oxygen abundance using the calibrator Kew02_N2O2 at 1 Re |
333 | e_OH_Kew02_N2O2_Re_fit | dex | Error in the oxygen abundance using the calibrator Kew02_N2O2 at 1 Re |
334 | OH_Kew02_N2O2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Kew02_N2O2 |
335 | e_OH_Kew02_N2O2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Kew02_N2O2 |
336 | OH_Pil10_ONS_Re_fit | dex | Oxygen abundance using the calibrator Pil10_ONS at 1 Re |
337 | e_OH_Pil10_ONS_Re_fit | dex | Error in the oxygen abundance using the calibrator Pil10_ONS at 1 Re |
338 | OH_Pil10_ONS_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pil10_ONS |
339 | e_OH_Pil10_ONS_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pil10_ONS |
340 | OH_Pil10_ON_Re_fit | dex | Oxygen abundance using the calibrator Pil10_ON at 1 Re |
341 | e_OH_Pil10_ON_Re_fit | dex | Error in the oxygen abundance using the calibrator Pil10_ON at 1 Re |
342 | OH_Pil10_ON_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pil10_ON |
343 | e_OH_Pil10_ON_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pil10_ON |
344 | OH_Pil11_NS_Re_fit | dex | Oxygen abundance using the calibrator Pil11_NS at 1 Re |
345 | e_OH_Pil11_NS_Re_fit | dex | Error in the oxygen abundance using the calibrator Pil11_NS at 1 Re |
346 | OH_Pil11_NS_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pil11_NS |
347 | e_OH_Pil11_NS_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pil11_NS |
348 | OH_Cur20_RS32_Re_fit | dex | Oxygen abundance using the calibrator Cur20_RS32 at 1 Re |
349 | e_OH_Cur20_RS32_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_RS32 at 1 Re |
350 | OH_Cur20_RS32_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_RS32 |
351 | e_OH_Cur20_RS32_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_RS32 |
352 | OH_Cur20_R3_Re_fit | dex | Oxygen abundance using the calibrator Cur20_R3 at 1 Re |
353 | e_OH_Cur20_R3_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_R3 at 1 Re |
354 | OH_Cur20_R3_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_R3 |
355 | e_OH_Cur20_R3_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_R3 |
356 | OH_Cur20_O3O2_Re_fit | dex | Oxygen abundance using the calibrator Cur20_O3O2 at 1 Re |
357 | e_OH_Cur20_O3O2_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_O3O2 at 1 Re |
358 | OH_Cur20_O3O2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_O3O2 |
359 | e_OH_Cur20_O3O2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_O3O2 |
360 | OH_Cur20_S2_Re_fit | dex | Oxygen abundance using the calibrator Cur20_S2 at 1 Re |
361 | e_OH_Cur20_S2_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_S2 at 1 Re |
362 | OH_Cur20_S2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_S2 |
363 | e_OH_Cur20_S2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_S2 |
364 | OH_Cur20_R2_Re_fit | dex | Oxygen abundance using the calibrator Cur20_R2 at 1 Re |
365 | e_OH_Cur20_R2_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_R2 at 1 Re |
366 | OH_Cur20_R2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_R2 |
367 | e_OH_Cur20_R2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_R2 |
368 | OH_Cur20_N2_Re_fit | dex | Oxygen abundance using the calibrator Cur20_N2 at 1 Re |
369 | e_OH_Cur20_N2_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_N2 at 1 Re |
370 | OH_Cur20_N2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_N2 |
371 | e_OH_Cur20_N2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_N2 |
372 | OH_Cur20_R23_Re_fit | dex | Oxygen abundance using the calibrator Cur20_R23 at 1 Re |
373 | e_OH_Cur20_R23_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_R23 at 1 Re |
374 | OH_Cur20_R23_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_R23 |
375 | e_OH_Cur20_R23_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_R23 |
376 | OH_Cur20_O3N2_Re_fit | dex | Oxygen abundance using the calibrator Cur20_O3N2 at 1 Re |
377 | e_OH_Cur20_O3N2_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_O3N2 at 1 Re |
378 | OH_Cur20_O3N2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_O3N2 |
379 | e_OH_Cur20_O3N2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_O3N2 |
380 | OH_Cur20_O3S2_Re_fit | dex | Oxygen abundance using the calibrator Cur20_O3S2 at 1 Re |
381 | e_OH_Cur20_O3S2_Re_fit | dex | Error in the oxygen abundance using the calibrator Cur20_O3S2 at 1 Re |
382 | OH_Cur20_O3S2_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Cur20_O3S2 |
383 | e_OH_Cur20_O3S2_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Cur20_O3S2 |
384 | OH_KK04_Re_fit | dex | Oxygen abundance using the calibrator KK04 at 1 Re |
385 | e_OH_KK04_Re_fit | dex | Error in the oxygen abundance using the calibrator KK04 at 1 Re |
386 | OH_KK04_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator KK04 |
387 | e_OH_KK04_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator KK04 |
388 | OH_Pil16_R_Re_fit | dex | Oxygen abundance using the calibrator Pil16_R at 1 Re |
389 | e_OH_Pil16_R_Re_fit | dex | Error in the oxygen abundance using the calibrator Pil16_R at 1 Re |
390 | OH_Pil16_R_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pil16_R |
391 | e_OH_Pil16_R_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pil16_R |
392 | OH_Pil16_S_Re_fit | dex | Oxygen abundance using the calibrator Pil16_S at 1 Re |
393 | e_OH_Pil16_S_Re_fit | dex | Error in the oxygen abundance using the calibrator Pil16_S at 1 Re |
394 | OH_Pil16_S_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Pil16_S |
395 | e_OH_Pil16_S_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Pil16_S |
396 | OH_Ho_Re_fit | dex | Oxygen abundance using the calibrator Ho at 1 Re |
397 | e_OH_Ho_Re_fit | dex | Error in the oxygen abundance using the calibrator Ho at 1 Re |
398 | OH_Ho_alpha_fit | dex/Re | Slope of the O/H gradient using the calibrator Ho |
399 | e_OH_Ho_alpha_fit | dex/Re | Error in the slope of the O/H gradient using the calibrator Ho |
400 | U_Dors_O32_Re_fit | dex | log(U) ionization parameter using the calibrator Dors_O32 at 1 Re |
401 | e_U_Dors_O32_Re_fit | dex | Error in log(U) using the calibrator Dors_O32 at 1 Re |
402 | U_Dors_O32_alpha_fit | dex/Re | Slope of the log(U) gradient using the calibrator Dors_O32 |
403 | e_U_Dors_O32_alpha_fit | dex/Re | Error in the slope of the log(U) gradient using the calibrator Dors_O32 |
404 | U_Dors_S_Re_fit | dex | log(U) ionization parameter using the calibrator Dors_S at 1 Re |
405 | e_U_Dors_S_Re_fit | dex | Error in log(U) using the calibrator Dors_S at 1 Re |
406 | U_Dors_S_alpha_fit | dex/Re | Slope of the log(U) gradient using the calibrator Dors_S |
407 | e_U_Dors_S_alpha_fit | dex/Re | Error in the slope of the log(U) gradient using the calibrator Dors_S |
408 | U_Mor16_O23_fs_Re_fit | dex | log(U) ionization parameter using the calibrator Mor16_O23_fs at 1 Re |
409 | e_U_Mor16_O23_fs_Re_fit | dex | Error in log(U) using the calibrator Mor16_O23_fs at 1 Re |
410 | U_Mor16_O23_fs_alpha_fit | dex/Re | Slope of the log(U) gradient using the calibrator Mor16_O23_fs |
411 | e_U_Mor16_O23_fs_alpha_fit | dex/Re | Error in the slope of the log(U) gradient using the calibrator Mor16_O23_fs |
412 | U_Mor16_O23_ts_Re_fit | dex | log(U) ionization parameter using the calibrator Mor16_O23_ts at 1 Re |
413 | e_U_Mor16_O23_ts_Re_fit | dex | Error in log(U) using the calibrator Mor16_O23_ts at 1 Re |
414 | U_Mor16_O23_ts_alpha_fit | dex/Re | Slope of the log(U) gradient using the calibrator Mor16_O23_ts |
415 | e_U_Mor16_O23_ts_alpha_fit | dex/Re | Error in the slope of the log(U) gradient using the calibrator Mor16_O23_ts |
416 | NH_Pil16_R_Re_fit | dex | Nitrogen abundance using the calibrator Pil16_R at 1 Re |
417 | e_NH_Pil16_R_Re_fit | dex | Error in the nitrogen abundance using the calibrator Pil16_R at 1 Re |
418 | NH_Pil16_R_alpha_fit | dex/Re | Slope of the N/H gradient using the calibrator Pil16_R |
419 | e_NH_Pil16_R_alpha_fit | dex/Re | Error in the slope of the N/H gradient using the calibrator Pil16_R |
420 | NO_Pil16_R_Re_fit | dex | N/O abundance using the calibrator Pil16_R at 1 Re |
421 | e_NO_Pil16_R_Re_fit | dex | Error in the N/O abundance using the calibrator Pil16_R at 1 Re |
422 | NO_Pil16_R_alpha_fit | dex/Re | Slope of the N/O gradient using the calibrator Pil16_R |
423 | e_NO_Pil16_R_alpha_fit | dex/Re | Error in the slope of the N/O gradient using the calibrator Pil16_R |
424 | NO_Pil16_Ho_R_Re_fit | dex | N/O abundance using the calibrator Pil16_Ho_R at 1 Re |
425 | e_NO_Pil16_Ho_R_Re_fit | dex | Error in the N/O abundance using the calibrator Pil16_Ho_R at 1 Re |
426 | NO_Pil16_Ho_R_alpha_fit | dex/Re | Slope of the N/O gradient using the calibrator Pil16_Ho_R |
427 | e_NO_Pil16_Ho_R_alpha_fit | dex/Re | Error in the slope of the N/O gradient using the calibrator Pil16_Ho_R |
428 | NO_Pil16_N2_R2_Re_fit | dex | N/O abundance using the calibrator Pil16_N2_R2 at 1 Re |
429 | e_NO_Pil16_N2_R2_Re_fit | dex | Error in the N/O abundance using the calibrator Pil16_N2_R2 at 1 Re |
430 | NO_Pil16_N2_R2_alpha_fit | dex/Re | Slope of the N/O gradient using the calibrator Pil16_N2_R2 |
431 | e_NO_Pil16_N2_R2_alpha_fit | dex/Re | Error in the slope of the N/O gradient using the calibrator Pil16_N2_R2 |
432 | Ne_Oster_S_Re_fit | dex | Electron density using the Oster_S estimator at 1 Re |
433 | e_Ne_Oster_S_Re_fit | dex | Error in n_e using the Oster_S estimator at 1 Re |
434 | Ne_Oster_S_alpha_fit | dex/Re | Slope of the n_e gradient using the Oster_S estimator |
435 | e_Ne_Oster_S_alpha_fit | dex/Re | Error in the slope of n_e gradient using the Oster_S estimator |
436 | Hd_Re_fit | Å | Value of the Hd stellar index at 1 Re |
437 | e_Hd_Re_fit | Å | Error of the Hd stellar index at 1 Re |
438 | Hd_alpha_fit | Å/Re | Slope of the gradient of the Hd index |
439 | e_Hd_alpha_fit | Å/Re | Error of the slope of the gradient of the Hd index |
440 | Hb_Re_fit | Å | Value of the Hβ stellar index at 1 Re |
441 | e_Hb_Re_fit | Å | Error of the Hβ stellar index at 1 Re |
442 | Hb_alpha_fit | Å/Re | Slope of the gradient of the Hβ index |
443 | e_Hb_alpha_fit | Å/Re | Error of the slope of the gradient of the Hβ index |
444 | Mgb_Re_fit | Å | Value of the Mgb stellar index at 1 Re |
445 | e_Mgb_Re_fit | Å | Error of the Mgb stellar index at 1 Re |
446 | Mgb_alpha_fit | Å/Re | Slope of the gradient of the Mgb index |
447 | e_Mgb_alpha_fit | Å/Re | Error of the slope of the gradient of the Mgb index |
448 | Fe5270_Re_fit | Å | Value of the Fe5270 stellar index at 1 Re |
449 | e_Fe5270_Re_fit | Å | Error of the Fe5270 stellar index at 1 Re |
450 | Fe5270_alpha_fit | Å/Re | Slope of the gradient of the Fe5270 index |
451 | e_Fe5270_alpha_fit | Å/Re | Error of the slope of the gradient of the Fe5270 index |
452 | Fe5335_Re_fit | Å | Value of the Fe5335 stellar index at 1 Re |
453 | e_Fe5335_Re_fit | Å | Error of the Fe5335 stellar index at 1 Re |
454 | Fe5335_alpha_fit | Å/Re | Slope of the gradient of the Fe5335 index |
455 | e_Fe5335_alpha_fit | Å/Re | Error of the slope of the gradient of the Fe5335 index |
456 | D4000_Re_fit1 | ⋯ | Value of the D4000 stellar index at 1 Re |
457 | e_D4000_Re_fit | ⋯ | Error of the D4000 stellar index at 1 Re |
458 | D4000_alpha_fit | ⋯ | Slope of the gradient of the D4000 index |
459 | e_D4000_alpha_fit | ⋯ | Error of the slope of the gradient of the D4000 index |
460 | Hdmod_Re_fit | Å | Value of the Hdmod stellar index at 1 Re |
461 | e_Hdmod_Re_fit | Å | Error of the Hdmod stellar index at 1 Re |
462 | Hdmod_alpha_fit | Å/Re | Slope of the gradient of the Hdmod index |
463 | e_Hdmod_alpha_fit | Å/Re | Error of the slope of the gradient of the Hdmod index |
464 | Hg_Re_fit | Å | Value of the Hg stellar index at 1 Re |
465 | e_Hg_Re_fit | Å | Error of the Hg stellar index at 1 Re |
466 | Hg_alpha_fit | Å/Re | Slope of the gradient of the Hg index |
467 | e_Hg_alpha_fit | Å/Re | Error of the slope of the gradient of the Hg index |
468 | u_band_mag | mag | u-band magnitude derived from the original cube |
469 | u_band_mag_error | mag | Error in the u-band magnitude derived from the original cube |
470 | u_band_abs_mag | mag | u-band abs. magnitude derived from the original cube |
471 | u_band_abs_mag_error | mag | Error in the u-band abs. magnitude derived from the original cube |
472 | g_band_mag | mag | g-band magnitude derived from the original cube |
473 | g_band_mag_error | mag | Error in the g-band magnitude derived from the original cube |
474 | g_band_abs_mag | mag | g-band abs. magnitude derived from the original cube |
475 | g_band_abs_mag_error | mag | Error in the g-band abs. magnitude derived from the original cube |
476 | r_band_mag | mag | r-band magnitude derived from the original cube |
477 | r_band_mag_error | mag | Error in the r-band magnitude derived from the original cube |
478 | r_band_abs_mag | mag | r-band abs. magnitude derived from the original cube |
479 | r_band_abs_mag_error | mag | Error in the r-band abs. magnitude derived from the original cube |
480 | i_band_mag | mag | i-band magnitude derived from the original cube |
481 | i_band_mag_error | mag | Error in the i-band magnitude derived from the original cube |
482 | i_band_abs_mag | mag | i-band abs. magnitude derived from the original cube |
483 | i_band_abs_mag_error | mag | Error in the i-band abs. magnitude derived from the original cube |
484 | B_band_mag | mag | B-band magnitude derived from the original cube |
485 | B_band_mag_error | mag | Error in the B-band magnitude derived from the original cube |
486 | B_band_abs_mag | mag | B-band abs. magnitude derived from the original cube |
487 | B_band_abs_mag_error | mag | Error in the B-band abs. magnitude derived from the original cube |
488 | V_band_mag | mag | V-band magnitude derived from the original cube |
489 | V_band_mag_error | mag | Error in the V-band magnitude derived from the original cube |
490 | V_band_abs_mag | mag | V-band abs. magnitude derived from the original cube |
491 | V_band_abs_mag_error | mag | Error in V-band abs. magnitude derived from the original cube |
492 | RJ_band_mag | mag | R-band magnitude derived from the original cube |
493 | RJ_band_mag_error | mag | Error in the R-band magnitude derived from the original cube |
494 | RJ_band_abs_mag | mag | R-band abs. magnitude derived from the original cube |
495 | RJ_band_abs_mag_error | mag | Error in the R-band abs. magnitude derived from the original cube |
496 | R50 | arcsec | Radius at which half of the light within the FOV in the g-band is integrated |
497 | error_R50 | arcsec | Error in R50 |
498 | R90 | arcsec | Radius at which 90% of the light within the FOV in the g-band is integrated |
499 | error_R90 | arcsec | Error in R90 |
500 | C | ⋯ | R90/R50 concentration index |
501 | e_C | ⋯ | Error in the concentration index |
502 | B − V | mag | B − V color |
503 | error_B − V | mag | Error in the B − V color |
504 | B − R | mag | B − R color |
505 | error_B-R | mag | Error in the B − R color |
506 | log_Mass_phot | log(M⊙) | Stellar masses derived from the photometric data within the FOV |
507 | e_log_Mass_phot | dex | Error in the stellar masses derived from the photometric data |
508 | V-band_SB_at_Re | mag/arcsec2 | V-band surface brightness at 1 Re |
509 | error_V-band_SB_at_Re | mag/arcsec2 | Error in the V-band surface brightness at 1 Re |
510 | V-band_SB_at_R_50 | mag/arcsec2 | V-band surface brightness at R50 |
511 | error_V-band_SB_at_R_50 | mag/arcsec2 | Error in the V-band surface brightness at R50 |
512 | nsa_sersic_n_morph | ⋯ | NSA Sérsic index |
513 | u − g | mag | u − g NSA color |
514 | g − r | mag | g − r NSA color |
515 | r − i | mag | r − i NSA color |
516 | i − z | mag | i − z NSA color |
517 | P(CD) | ⋯ | Probability of being a cD galaxy |
518 | P(E) | ⋯ | Probability of being an E galaxy |
519 | P(S0) | ⋯ | Probability of being an S0 galaxy |
520 | P(Sa) | ⋯ | Probability of being an Sa galaxy |
521 | P(Sab) | ⋯ | Probability of being an Sab galaxy |
522 | P(Sb) | ⋯ | Probability of being an Sb galaxy |
523 | P(Sbc) | ⋯ | Probability of being an Sbc galaxy |
524 | P(Sc) | ⋯ | Probability of being an Sc galaxy |
525 | P(Scd) | ⋯ | Probability of being an Scd galaxy |
526 | P(Sd) | ⋯ | Probability of being an Sd galaxy |
527 | P(Sdm) | ⋯ | Probability of being an Sdm galaxy |
528 | P(Sm) | ⋯ | Probability of being an Sm galaxy |
529 | P(Irr) | ⋯ | Probability of being an Irr galaxy |
530 | best_type_n | ⋯ | Best morphological type index based on the NN analysis |
531 | best_type | ⋯ | Morphological type derived by the NN analysis |
532 | nsa_nsaid | ⋯ | NSA ID |
533 | Vmax_w | Mpc−3 dex−1 | Weight for the volume correction in volume |
534 | Num_w | ⋯ | Weight for the volume correction in number |
535 | QCFLAG | ⋯ | QC flag: 0 = good, 2 = bad, >2 warning |
Note.
a Derived based on a linear fit to the abundance gradient.Footnotes
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A standard ΛCDM cosmology, with HO = 73 km s−1, ΩM = 0.3, and ΩΛ = 0.7, is adopted throughout this work.
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Petrosian r-band R50.
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For this parameter, we use the eccentricity, defined as
.
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The R band is denoted as "RJ" to avoid confusion with a radius.
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A similar figure is included in Neumann et al. (2022; Figure 11).
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