SDSS-IV MaNGA: Radial Gradients in Stellar Population Properties of Early-Type and Late-Type Galaxies
Taniya Parikh, Daniel Thomas, Claudia Maraston, Kyle B. Westfall, Brett H. Andrews, Nicholas Fraser Boardman, Niv Drory, Grecco Oyarzun
MMNRAS , 1–30 (2021) Preprint 15 February 2021 Compiled using MNRAS L A TEX style file v3.0
SDSS-IV MaNGA: Radial Gradients in Stellar Population Properties ofEarly-Type and Late-Type Galaxies
Taniya Parikh , ★ , Daniel Thomas , , Claudia Maraston , Kyle B. Westfall ,Brett H. Andrews , Nicholas Fraser Boardman , Niv Drory , Grecco Oyarzun Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse 1, 85748 Garching bei München, Germany Institute of Cosmology and Gravitation, University of Portsmouth, 1-8 Burnaby Road, Portsmouth PO1 3FX, UK School of Mathematics and Physics, University of Portsmouth, Lion Gate Building, Portsmouth, PO1 3HF, UK University of California Observatories, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA PITT PACC, Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA Department of Physics & Astronomy, University of Utah, Salt Lake City, UT, 84112, USA McDonald Observatory, The University of Texas at Austin, 1 University Station, Austin, TX 78712, USA Astronomy Department, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA
Accepted XXX. Received YYY; in original form ZZZ
ABSTRACT
We derive ages, metallicities, and individual element abundances of early- and late-type galaxies (ETGs and LTGs) out to 1.5 R 𝑒 .We study a large sample of 1900 galaxies spanning 8 . − . 𝑀 / 𝑀 (cid:12) in stellar mass, through key absorption featuresin stacked spectra from the SDSS-IV/MaNGA survey. We use mock galaxy spectra with extended star formation histories tovalidate our method for LTGs and use corrections to convert the derived ages into luminosity- and mass-weighted quantities.We find flat age and negative metallicity gradients for ETGs and negative age and negative metallicity gradients for LTGs.Age gradients in LTGs steepen with increasing galaxy mass, from − . ± .
11 log Gyr/R 𝑒 for the lowest mass galaxies to − . ± .
08 log Gyr/R 𝑒 for the highest mass ones. This strong gradient-mass relation has a slope of − . ± .
18. Comparinglocal age and metallicity gradients with the velocity dispersion 𝜎 within galaxies against the global relation with 𝜎 shows thatinternal processes regulate metallicity in ETGs but not age, and vice versa for LTGs. We further find that metallicity gradientswith respect to local 𝜎 show a much stronger dependence on galaxy mass than radial metallicity gradients. Both galaxy typesdisplay flat [C/Fe] and [Mg/Fe], and negative [Na/Fe] gradients, whereas only LTGs display gradients in [Ca/Fe] and [Ti/Fe].ETGs have increasingly steep [Na/Fe] gradients with local 𝜎 reaching 6 . ± .
78 dex/log km/s for the highest masses. [Na/Fe]ratios are correlated with metallicity for both galaxy types across the entire mass range in our sample, providing support formetallicity dependent supernova yields.
Key words: galaxies: abundances – galaxies: stellar content – galaxies: elliptical and lenticular, cD – galaxies: formation –galaxies: evolution
Stellar population analysis is a powerful tool for extracting physicalparameters from galaxy spectra, often referred to as extragalacticarcheology. Results can be obtained via modelling the full spectrum(Cid Fernandes et al. 2005; Ocvirk et al. 2006; Koleva et al. 2009;Conroy et al. 2014; Cappellari 2017; Goddard et al. 2017; Conroyet al. 2018; Wilkinson et al. 2017) or absorption index measurements(Trager et al. 2000; Proctor & Sansom 2002; Thomas et al. 2005;Schiavon 2007; Thomas et al. 2010; Johansson et al. 2012) whichrespond to a combination of stellar population parameters. Throughthis we have learnt that more massive galaxies contain older, moremetal rich populations (Kuntschner 2000; Thomas et al. 2005) and ★ E-mail: [email protected] appear to have formed their stars earliest (Trager et al. 1998; Thomaset al. 2005; Bernardi et al. 2006; Clemens et al. 2006).High resolution data and state-of-the-art stellar population modelshave given way to detailed studies of abundance patterns in early typegalaxies. A wealth of information can be obtained by modelling theabundances of individual elements. Furthermore, spatially resolvedspectroscopy has led to studies of these properties as a function ofgalaxy radius. Abundance gradients within galaxies provide addi-tional information on the processes which regulate the growth andassembly of galaxies. Through this we can learn about quenching,mergers, and inside-out or outside-in formation. Stellar yields outlinethe amount of each element produced by stars of different masses,and is a basic parameter for chemical evolution models which canbe compared to observations in order to place constraints. Gradi-ents also provide information on internal processes such as stellarmigration, inflow of pristine gas into galaxies, outflows of enriched © a r X i v : . [ a s t r o - ph . GA ] F e b T. Parikh et al. material via supernovae and stellar winds into the ISM (see Maiolino& Mannucci 2019, for a review)The IMF of galaxies is a hot topic in recent literature. Studiesare increasingly finding evidence for the IMF becoming more bot-tom heavy with increasing galaxy mass for ETGs (Cappellari et al.2012; Conroy & van Dokkum 2012b; Spiniello et al. 2012; Ferreraset al. 2013; La Barbera et al. 2013; Lyubenova et al. 2016). Addi-tionally, measuring the IMF within massive ETGs points towards abottom-heavy IMF in galaxy centres, and a Kroupa IMF at larger radii(Martín-Navarro et al. 2015; La Barbera et al. 2016; van Dokkumet al. 2017; La Barbera et al. 2017; Parikh et al. 2018; DomínguezSánchez et al. 2019; Bernardi et al. 2019). Vaughan et al. (2018) findthat their data cannot conclusively rule out IMF gradients, but thattrends can also be explained by abundance variations, while Altonet al. (2017, 2018) also find no IMF gradients for a small sample ofgalaxies. Furthermore, constraints from four massive lensed galax-ies, with velocity dispersions of >400 km/s, reveal a mass-to-lightratio corresponding to a Kroupa IMF (Smith et al. 2015; Collier et al.2018).Focussing on possible systematics in stellar population analysis,for near-infrared gravity-sensitive features that are highly sensitive tochanges in the IMF, the difference between a Kroupa and a SalpeterIMF can be on the percent level, and the degeneracies between differ-ent parameters remains the largest uncertainty. Hence the certaintyin determining the age, metallicity and chemical abundances directlyimpacts the accuracy of the determined IMF.Most of literature has focused on ETGs to carry out detailed stellarpopulation analysis. This is because they can be better approximatedby simple star formation histories with no ongoing star formation.ETGs are known to be composed of mostly old stars, with little orno current star formation while LTGs can have complicated star for-mation histories (Kauffmann et al. 2003). Additionally, absorptionfeatures in LTGs can be affected by moderate to severe contributionto absorption features from the interstellar medium, depending onthe viewing angle. Still, there are studies using full spectrum fitting,which have extracted the luminosity-weighted (LW) ages and metal-licities of LTGs (Sánchez-Blázquez et al. 2014; González Delgadoet al. 2015; Goddard et al. 2017; Zheng et al. 2017).When considering LTGs, which are known to be composed ofdifferent stellar populations, it would be prudent to consider the starformation history, and represent the galaxies using composite stellarpopulations (CSPs). However, it is non-trivial to derive accurate starformation histories and computationally expensive to explore the fullposterior distribution. Spectral fitting codes have attempted to findwork-around options, e.g. VESPA includes models with increasinglevels of complexity that are used as and when data require them(Tojeiro et al. 2009), STARLIGHT fits using full set of models andthen restricts the parameter space to a coarser grid (Cid Fernandaset al. 2005), and FIREFLY uses liberal parameter searching witha convergence test while obtaining linear combinations of best-fitmodels, to avoid falling into local minima (Wilkinson et al. 2017).However, none of these fitting approaches include a detailed accountof element abundance ratios, which is instead the scope of our paper.Studies have shown that simple stellar population (SSP)-equivalentparameters can still provide useful information when analysingcomplex stellar populations (e.g. Serra & Trager 2007; Trager &Somerville 2009; Pforr et al. 2012; Citro et al. 2016; Leethochawalitet al. 2018). We should however apply the correct interpretation ofthe age. The age derived through the SSP-based analysis will not cor-respond to the age of the whole galaxy, rather it will be closer to theone of the latest stellar generation. Therefore the SSP-based analysisquantifies the relative proportion of young populations in the galaxy. Similarly, the derived chemical composition may reflect the one ofthe latest generations, thereby hiding the oldest, more metal-poorcomponents. Abundance-ratio analysis may actually allow us to pullout these hidden generations. In this work we use composite modelsto quantify bias in our analysis, as described below.Parikh et al. (2018, P18); Parikh et al. (2019, P19) obtained stellarpopulation properties for a sample of 366 ETGs out to the half-light radius using data from Mapping Nearby Galaxies at ApachePoint Observatory (MaNGA Bundy et al. 2015), part of the SloanDigital Sky Survey IV (Blanton et al. 2017). In the present work,we make use of a subsequent date release to extend results fromP19 in mass, radius, and morphology. For the first time, we attemptto include LTGs in our analysis. We measure absorption featuresfor LTGs and derive SSP-equivalent stellar population parameters.Since the light is dominated by young stars, we expect to be biasedtowards lower ages. We perform extensive testing using mock CSPmodels with an exponentially decaying SFH to show that luminosity-weighted (LW) and mass-weighted (MW) ages can be obtained aftera correction, while derived metallicities and abundances are goodtracers of the underlying parameters. We derive ages and metallicitiesconsistent with literature, and use these to constrain abundances ofchemical elements. Although considerable progress is required onthe modelling to be able to accurately determine such parametersfor LTGs, we present this work as an indication of detailed stellarpopulation signatures in spiral galaxies.This paper is outlined as follows: the new sample selection,changes to the stacking, and a revised method of deriving abundances,are described in Section 2. Next, the results of our stellar populationmodel fittings to the measured absorption indices on ETG and LTGstacked spectra are presented in Section 3. We discuss our findingsin Section 4, focussing on differences between the two galaxy types,and spatial variations. This Section also includes a comparison ofour results for LTGs to the abundances obtained from studying ourown Galaxy. Finally, a summary is given in Section 5.
MaNGA is an ongoing project obtaining spatially resolved spec-troscopy for 10,000 nearby galaxies at a spectral resolution of 𝑅 ∼ , − ,
300 Å. Indepen-dent fibre-bundles provide 17 simultaneous observations of galaxies(Drory et al. 2015), which are fed into the BOSS spectrographs(Smee et al. 2013) on the Sloan 2 . 𝑟 -band isophotal ellipticity, position an-gle, half-light radius, and galaxy mass (based on a Chabrier IMF,Chabrier 2003) from this catalogue.Optical fibre bundles of different sizes are chosen to ensure allgalaxies are covered out to at least 1 . 𝑅 e for the ‘Primary’ and ‘Color-enhanced’ samples, together known as ‘Primary + ’, and to 2 . 𝑅 e forthe ‘Secondary’ sample (Wake et al. 2017). The Color-enhancedsample supplements colour space that is otherwise under-representedrelative to the overall galaxy population. The spatial resolution is1 − 𝑧 ∼ . 𝑟 -band S/N is 4 − − , for each 2 (cid:48)(cid:48) fibre, at the outskirts of MaNGAgalaxies. For more detail on the survey we refer the reader to Lawet al. (2015) for MaNGA’s observing strategy, to Yan et al. (2016a)for the spectrophotometry calibration, to Wake et al. (2017) for thesurvey design, and to Yan et al. (2016b) for the initial performance. MNRAS , 1–30 (2021) patially-resolved stellar population properties Figure 1.
Left: Colour-mass diagram for our selection of ETGs (red) and LTGs (blue) after applying S/N and inclination cuts to galaxies from SD15. The massescome from the NASA Sloan Atlas catalogue (NSA, Blanton et al. 2005) and the morphologies are based on a Deep Learning Value Added Catalog (Fischeret al. 2019). Right: The stellar mass distributions for the two galaxy types are shown, with the ETG sample from P19 overlaid in grey.
Figure 2.
Distribution of inclinations for the spiral galaxies. We excludegalaxies with inclinations greater than 60 degrees in order to select mostlyface-on objects, since these have a smaller ISM path-length and are affectedby contamination to a lesser extent.
Using the general stacking technique described in P18, we proceedto bin ETGs and LTGs from the latest SDSS data release out to 1.5R 𝑒 . There are several improvements we have made to the sampleselection and binning method, which are described below. (i) The latest SDSS-IV data release with new MaNGA data, DR15(Aguado et al. 2019), contains 4672 datacubes, allowing us to con-siderably increase our sample size and parameter space.(ii) We stack spectra of individual spaxels, but make use of kine-matics determined for Voronoi-binned spectra to a S/N of 10 fromMaNGA’s data analysis pipeline (DAP Westfall et al. 2019). Hence,individual spectra belonging to the same Voronoi bin would have thesame stellar velocity and velocity dispersion. The benefit of adopt-ing this approach is to avoid using unreliable kinematics for lowerS/N spectra. We impose a S/N > 5 pixel − threshold for individualspectra. In P18, since we used kinematics for individual spectra, wehad to impose a higher S/N threshold of 7 pixel − to ensure reli-able kinematics, leading to more data loss. In the present approach, Table 1.
We split our sample of ETGs (top panel) into six stellar mass binsand LTGs (bottom panel) into seven bins. For each bin, the number of galaxiesand median velocity dispersion, effective radius and redshift are given.Mass range (log 𝑀 / 𝑀 (cid:12) ) Number 𝜎 (km/s) 𝑅 e (kpc) z8.8 - 9.8 148 72 3.74 0.0239.8 - 10.3 150 144 3.89 0.02710.3 - 10.6 149 188 4.99 0.03110.6 - 10.8 149 227 6.02 0.04010.8 - 11.0 150 250 6.27 0.06311.0 - 11.3 149 272 5.95 0.0998.6 - 9.2 143 55 4.98 0.0209.2 - 9.6 143 65 5.80 0.0249.6 - 9.9 144 66 6.37 0.0269.9 - 10.1 144 92 6.29 0.02710.1 - 10.3 144 117 7.84 0.02910.3 - 10.6 143 139 7.75 0.03310.6 - 11.2 144 177 7.80 0.057 lowering our S/N criteria allows a large sample that does not affectthe accuracy of the kinematics. Stellar velocities are used along withthe galaxy redshift to bring individual spectra to the rest frame, anddispersions are propagated to obtain profiles for the final stackedspectra. Emission line fitting and subtraction, and correcting for ve-locity dispersion broadening, are carried out after stacking. See P18for a detailed description of these procedures.(iii) For morphological classifications, we make use of the DeepLearning-based catalog (Fischer et al. 2019), which provides theT-type for each galaxy such that T-type >0 gives late types, whileT-type <0 gives early types, including ellipticals and S0s. We choosethis classification since Fischer et al. (2019) show that galaxies with ahigh probability of being ETGs from Galaxy Zoo can be significantlycontaminated by LTGs. The left hand panel of Fig. 1 shows our finalsample in a colour-mass plot, resulting in 895 early type, and 1005 latetype galaxies, ranging in stellar mass from 8 . − .
33 log 𝑀 / 𝑀 (cid:12) .The two types occupy different regions of this parameter space, withearly types having redder g - i colour and extending to higher masses.The late type galaxies on the other hand display a greater spread in g- i. The right hand panel shows the mass histograms, with the limited MNRAS , 1–30 (2021)
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Figure 3.
Example images of a LTG, MaNGA-ID 1-591917, (left) and an ETG, MaNGA-ID 1-314719 (right) from our sample. The magenta hexagon showsMaNGA’s IFU bundle. Overlaid in white are the radial bins calculated from the position angle and ellipticity of the galaxy, out to 1.5 R 𝑒 . The bins are in stepsof 0.1R 𝑒 in the centre, with increased widths in the outermost regions in order to compensate for the decreasing surface brightness. sample of ETGs from P18 overlaid. The increased mass range that wenow study is a substantial improvement. These final distributions areaffected by the various cuts, but generally late types are less massivethan early types.(iv) We split these into 6 ETG mass bins and 7 LTG mass bins,with the boundaries calculated such that there are an equal numberof galaxies ( ∼ 𝑒 .Images of a spiral and an elliptical galaxy are shown in Fig. 3 withthe new elliptical annuli bins overlaid. The MaNGA coverage is outto 1.5 R 𝑒 , and our final three radial bins are twice or thrice as wideas the inner ones.(vii) P18 calculated the error spectrum by taking the standard de-viation of the radially stacked spectra from each galaxy, which wouldgo into the stacked spectrum for a particular mass bin. However thismethod did not give weight to the number of spectra that each galaxyhad originally contributed, leading to errors being overestimated. Torectify this, we now take the standard deviation of all raw spectracontributing to a stack. We discuss the effect on the errors in Sec-tion 3.Stacked spectra from the central 0.1 R 𝑒 of the two galaxy typesfor an intermediate mass bin, containing ∼
150 galaxies, are shownin Fig. 4. The differences in the spectra of the two galaxy types areevident i.e. LTGs have strong emission lines and the continuum flux level is much lower. The bottom panel shows the same spectra afterfitting for and subtracting emission lines using the penalised pixel-fitting algorithm, pPXF (Cappellari & Emsellem 2004). The first fitprovides the stellar continuum and stellar kinematics, and the secondfit to the spectrum after subtracting the continuum provides a fit for21 emission lines (Kramida et al. 2018). After subtracting emissionlines and normalising the spectra to the same flux scale, we can seethe same absorption features also present in the LTG spectra, andthat ETGs are more flux-enhanced at redder wavelengths comparedto LTGs. The MaNGA DAP provides emission line fits for individualspaxels however we fit for and subtract emission lines using pPXFourselves after stacking spectra to take advantage of the higher S/Nratio.
The velocity dispersion profiles of our binned galaxy sample areshown in Fig. 5. These values are calculated as the median of the ve-locity dispersions from the DAP of each spectrum in a particular bin.We use these values as the local 𝜎 in our analysis to look at correla-tions between stellar population parameters and velocity dispersions.These differ from the velocity dispersions measured on spectra afterstacking at large radii, due to velocity errors becoming convolvedas an effective dispersion (P18, Section 2.3). The profiles show ex-pected decreasing trends, except for the lowest mass LTGs, whichrise beyond 0.5 R 𝑒 . We do not include this mass bin in our analysisbecause of large uncertainties in H 𝛽 absorption measurements, asshown in Section 3.1.These velocity dispersion are a combination of bulge and diskcontributions. We do not attempt to separate the components in thiswork but include a discussion of other results in Section 4.3. The truephysical meaning of sigma needs to be investigated in an in-depthanalysis, however such a study goes well beyond the scope of thepresent paper. MNRAS000
The velocity dispersion profiles of our binned galaxy sample areshown in Fig. 5. These values are calculated as the median of the ve-locity dispersions from the DAP of each spectrum in a particular bin.We use these values as the local 𝜎 in our analysis to look at correla-tions between stellar population parameters and velocity dispersions.These differ from the velocity dispersions measured on spectra afterstacking at large radii, due to velocity errors becoming convolvedas an effective dispersion (P18, Section 2.3). The profiles show ex-pected decreasing trends, except for the lowest mass LTGs, whichrise beyond 0.5 R 𝑒 . We do not include this mass bin in our analysisbecause of large uncertainties in H 𝛽 absorption measurements, asshown in Section 3.1.These velocity dispersion are a combination of bulge and diskcontributions. We do not attempt to separate the components in thiswork but include a discussion of other results in Section 4.3. The truephysical meaning of sigma needs to be investigated in an in-depthanalysis, however such a study goes well beyond the scope of thepresent paper. MNRAS000 , 1–30 (2021) patially-resolved stellar population properties Figure 4.
Stacked spectra of ∼
150 LTGs (blue) and ETGs (red) from the central 0.1 R 𝑒 . The bottom panel shows the normalised spectra after fitting for andsubtracting emission lines using pPXF. Figure 5.
Velocity dispersion profiles for ETGs and LTGs are shown. Different colours represent different mass bins, as denoted in the legends.MNRAS , 1–30 (2021)
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Figure 6.
Flowchart showing the method adopted to derive chemical abun-dances. An initial fit is obtained using H 𝛽 , Mg 𝑏 , and
CSP ages plotted against derived age from fitting optical Lickindices with SSPs. Different colours show different 𝜏 models with an expo-nentially declining SFR. The age is always underestimated, and the effect islarger for greater 𝜏 models. Key absorption features are measured and corrected for velocity-dispersion broadening, and are modelled using Thomas et al. (2011a,TMJ) line index models. These models are an update and extensionof the earlier models by Thomas et al. (2003a); Thomas et al. (2004)and based on the Maraston (1998, 2005) evolutionary synthesis code,using new empirical calibrations by Johansson et al. (2010) basedon the MILES stellar library (Sánchez-Blázquez et al. 2006) andelement response functions from Korn et al. (2005). The models arecarefully calibrated with galactic globular clusters and reproduce theobservations well for the spectral featured used in this study (Thomaset al. 2011b).The models are available for different ages, metallicities, variableelement abundance ratios, and a Salpeter IMF, at MILES resolution.Element variation are calculated at constant total metallicity, hencethe TMJ models enhance the 𝛼 -elements and suppress the Fe-peakelements according to Equations 1-3 in Thomas et al. (2003a). The ages span from 0.1 - 15 Gyrs (in steps of 0.2 Gyrs up to 1Gyr,and then in steps of 1 Gyr), metallicities of -2.25, -1.35, -0.33, 0.0,+0.35, +0.67, [Mg/Fe] values of -0.3, 0.0, +0.3, +0.5 dex, and otherabundances ratios [X/Mg] of -0.3, 0.0, +0.3 dex. When fitting themodels, we set an upper limit on the age corresponding to the ageof the universe, 13.7 Gyrs, so that an age older than this is notallowed. Parameters are derived using chi-squared minimisation byinterpolating the SSP model grids in the n-dimensional parameterspace to fit the data.To compare measured absorption features to the TMJ models, wecorrect the equivalent widths to MILES resolution using correctionfactors. The procedure to derive these is described in detail in P18.Briefly, we measure absorption features on Maraston & Strömbäck(2011) models at MILES resolution and at the resolution of thedata, and use the ratio between these measurements as the correctionfactors.We follow our previous approach from P19 of modelling selectedfeatures in steps in order to break degeneracies between parameters.We make some modifications to follow the method in J12 moreclosely, by iterating using perturbed models until convergence isreached. This is summarised in a flowchart in Fig. 6. An initial fit iscarried out using H 𝛽 , Mg 𝑏 , Fe5270, Fe5335 to derive the parametersof age, metallicity, and [Mg/Fe]. We do not make use of higher orderBalmer lines since these are much more sensitive to [ 𝛼 /Fe] thanH 𝛽 (Thomas et al. 2004). Individual features reacting to differentelement abundances are then modelled in steps as before, C ± As mentioned in Section 1, LTGs have complicated star formationhistories, and modelling these with SSPs can lead to biases. Weuse CSP models to test our method and investigate this. These CSPmodels are constructed by integrating line indices over all the agescontributing to the composite population for different star formationrates. We assume an exponentially declining SFR, ∝ 𝑒𝑥 𝑝 (− 𝑡 / 𝜏 ) , and 𝜏 is the characteristic decay time. Such 𝜏 models were introducedby Bruzual A. (1983) and are used routinely for representing spiral MNRAS000
CSP ages plotted against derived age from fitting optical Lickindices with SSPs. Different colours show different 𝜏 models with an expo-nentially declining SFR. The age is always underestimated, and the effect islarger for greater 𝜏 models. Key absorption features are measured and corrected for velocity-dispersion broadening, and are modelled using Thomas et al. (2011a,TMJ) line index models. These models are an update and extensionof the earlier models by Thomas et al. (2003a); Thomas et al. (2004)and based on the Maraston (1998, 2005) evolutionary synthesis code,using new empirical calibrations by Johansson et al. (2010) basedon the MILES stellar library (Sánchez-Blázquez et al. 2006) andelement response functions from Korn et al. (2005). The models arecarefully calibrated with galactic globular clusters and reproduce theobservations well for the spectral featured used in this study (Thomaset al. 2011b).The models are available for different ages, metallicities, variableelement abundance ratios, and a Salpeter IMF, at MILES resolution.Element variation are calculated at constant total metallicity, hencethe TMJ models enhance the 𝛼 -elements and suppress the Fe-peakelements according to Equations 1-3 in Thomas et al. (2003a). The ages span from 0.1 - 15 Gyrs (in steps of 0.2 Gyrs up to 1Gyr,and then in steps of 1 Gyr), metallicities of -2.25, -1.35, -0.33, 0.0,+0.35, +0.67, [Mg/Fe] values of -0.3, 0.0, +0.3, +0.5 dex, and otherabundances ratios [X/Mg] of -0.3, 0.0, +0.3 dex. When fitting themodels, we set an upper limit on the age corresponding to the ageof the universe, 13.7 Gyrs, so that an age older than this is notallowed. Parameters are derived using chi-squared minimisation byinterpolating the SSP model grids in the n-dimensional parameterspace to fit the data.To compare measured absorption features to the TMJ models, wecorrect the equivalent widths to MILES resolution using correctionfactors. The procedure to derive these is described in detail in P18.Briefly, we measure absorption features on Maraston & Strömbäck(2011) models at MILES resolution and at the resolution of thedata, and use the ratio between these measurements as the correctionfactors.We follow our previous approach from P19 of modelling selectedfeatures in steps in order to break degeneracies between parameters.We make some modifications to follow the method in J12 moreclosely, by iterating using perturbed models until convergence isreached. This is summarised in a flowchart in Fig. 6. An initial fit iscarried out using H 𝛽 , Mg 𝑏 , Fe5270, Fe5335 to derive the parametersof age, metallicity, and [Mg/Fe]. We do not make use of higher orderBalmer lines since these are much more sensitive to [ 𝛼 /Fe] thanH 𝛽 (Thomas et al. 2004). Individual features reacting to differentelement abundances are then modelled in steps as before, C ± As mentioned in Section 1, LTGs have complicated star formationhistories, and modelling these with SSPs can lead to biases. Weuse CSP models to test our method and investigate this. These CSPmodels are constructed by integrating line indices over all the agescontributing to the composite population for different star formationrates. We assume an exponentially declining SFR, ∝ 𝑒𝑥 𝑝 (− 𝑡 / 𝜏 ) , and 𝜏 is the characteristic decay time. Such 𝜏 models were introducedby Bruzual A. (1983) and are used routinely for representing spiral MNRAS000 , 1–30 (2021) patially-resolved stellar population properties Figure 8.
LW Age, fitted metallicity and fitted [Mg/Fe] as a function of fitted age for a solar metallicity and solar abundance composite population. The fittedages correspond better to LW ages of a composite population rather than the oldest age present. At young ages, the metallicity is overestimated by up to +0.2 dex.The fitted [Mg/Fe] appears to be offset from the expected solar value by +0.15 dex at young ages. This decreases for older populations. The orange curves arepolynomial fits to the CSP results for all values of 𝜏 and the shaded orange regions represent the 2- 𝜎 uncertainty in the polynomial coefficients. This capturesthe scatter due to the different 𝜏 values. Figure 9.
Same as the left-hand panel of Fig. 8 for MW ages. galaxies (e.g. Bell & de Jong 2001). We use values of 𝜏 = 1, 1.5, 2,3 Gyr, and times t = 2 to 15 Gyr in steps of 1 Gyr. These 𝜏 valuescover the expected range for galaxies and the age range covers ourresults of fitted ages. The absorption index strength, I, is calculatedas follows: 𝐼 ( 𝑡, 𝜏 ) 𝐶𝑆𝑃 = ∫ 𝑡 exp − ( 𝑡 − 𝑡 (cid:48)) 𝜏 𝑀 / 𝐿 ( 𝑡 (cid:48) ) 𝐼 𝑆𝑆𝑃 𝑑𝑡 (cid:48) ∫ 𝑡 exp − ( 𝑡 − 𝑡 (cid:48)) 𝜏 𝑀 / 𝐿 ( 𝑡 (cid:48) ) 𝑑𝑡 (cid:48) , (1)where the SFR is at time t-t’, the time at which the populationformed, and M/L(t’) is the mass-to-light ratio for the stellar popula-tion age t’. The coordinate t traces time since the beginning of starformation, corresponding to the age of the oldest SSP contributing tothe composite population. We refer to this as the CSP age. More rel-evant quantities are LW and MW ages. In particular, analyses madeusing spectral indices correspond to LW quantities. These quantitiesare calculated by replacing 𝐼 𝑆𝑆𝑃 with age t’ and further removingthe M/L(t’) factors for the MW age.We apply the initial fit from Fig. 6 to the four optical CSP indicesand derive the age, metallicity, and [Mg/Fe]; these quantities derivedusing our methodology are referred to in this section as the fitted quantities. Shown in Fig. 7 is the difference between the CSP ageand fitted age as a function of the fitted age for a solar metallicityand solar abundance CSP. All ages are plotted in log Gyrs and the deviations from the dashed line indicate the effect of the extended starformation histories. These results show that fitting with SSPs leadsto ages being underestimated, and by a larger value for models withlarger 𝜏 (longer SF decay time) because these have a larger fractionof young stars. The bias is also a function of age and improves forolder populations.The left-hand panel of Fig. 8 shows the difference between the LWage and the fitted age. The differences from the true age are muchsmaller with a maximum of 0.3 dex. Again, ages are underestimatedfor larger 𝜏 values and for younger fitted ages. Also shown are thefitted metallicity (centre) and fitted Mg abundance (right) as a func-tion of the fitted age.Fitted ages are shown for >1 Gyr, the youngestfitted age for the galaxies in Section 3.The effect on metallicity and abundance is much smaller in com-parison. The metallicity is likely to be overestimated by up to 0.1dex for intermediate ages, while at very young, and older ages, thereis negligible difference. [Mg/Fe] is overestimated by even smallervalues of up to 0.05 dex. Additionally, repeating this exercise fordifferent metallicities suggests that these biases have a complicatedrelation with the true underlying parameters (see Appendix A), whilefor different abundances the corrections remain the same as for thesolar case i.e. negligible. Hence, we do not attempt to correct theseparameters. Furthermore, (Vazdekis et al. 2015) show that abundanceratios from indices are contributed mostly by old stars.Fig. 9 shows the equivalent correction for MW ages. In this case,fitted ages must be corrected by a larger value, and the correctionpeaks at the youngest ages. The LW age is equal to − . 𝑥 + . 𝑥 + . − . 𝑥 + . 𝑥 + . 𝜏 = 2 Gyr. The different colours represent different S/N.It is clear that the S/N does not affect the value of the parametersignificantly, but affects the error bars. The errors for S/N = 200 arevery small. Typical errors on the fitted parameters for S/N = 25 are MNRAS , 1–30 (2021)
T. Parikh et al.
Figure 10.
For 𝜏 = 1 Gyr, parameters are derived from mock CSPs with S/N values of 50, 100, and 200. The symbols show the ages, metallicities and [Mg/Fe]derived from the average index measurements on 100 realisations of perturbing the mocks, and the errors are propagated from the standard deviation of themeasured indices. The S/N does not affect the value of the derived parameter significantly, and at S/N = 200 (green), the errors are small. ± . ± . ± .
05 dex for[Mg/Fe].In conclusion, we find that SSP-equivalent ages for populationswith a complex SFH are underestimated compared to the true LW andMW ages. We construct functions using polynomial fits to CSPs toconvert our derived ages into these parameters. In comparison, SSP-equivalent [Z/H] and [Mg/Fe] are good tracers of the true metallicityand abundance.
This section presents the fits to the absorption features, and the de-rived stellar population parameters for ETGs and LTGs. The param-eters are first presented as a function of radius, and then as functionsof velocity dispersion and galaxy stellar mass. All absorption in-dex measurements and stellar population parameters are provided inAppendix B.
The absorption index measurements for ETGs are shown as symbolsin Fig. 11. There are 6 mass bins with low mass in yellow goingto high mass in red. All indices display negative radial gradients,with more massive galaxies showing stronger absorption and steeperprofiles. An exception to this is H 𝛽 , which increases with radius anddecreasing mass, in line with previous literature (trends with mass,Bender et al. 1992; Trager et al. 1998) (trends with radius, Carolloet al. 1993; Davies et al. 1993; Mehlert et al. 2000).The lowest mass bin has significantly weaker absorption comparedto the others (stronger for H 𝛽 ), this could be due to the large spreadin galaxy mass for this particular bin. We carried out a test to splitthis further into two mass bins with equal number of galaxies, andfind that the upper half 9 . − . 𝑀 / 𝑀 (cid:12) is still offset from theother masses, and has the same radial trends as the lower half 8 . − . 𝑀 / 𝑀 (cid:12) . This suggests a physical difference in absorptionstrengths for the lowest mass ETGs. Note also that NaD absorptionshows a jump for the lowest mass bin at ∼ . 𝑒 , while the othermass bins show a smooth decline in strength.Following the iterative procedure outlined above, we obtain best-fitting models to the measured absorption indices with small resid-uals. The model predictions for all the indices are shown as dashedlines; these are the final fits derived after constraining the stellarpopulation age, metallicity, and individual element abundances. The bottom panel shows the residuals between the data and models, di-vided by the dynamical range of each index. For CN1 which ismeasured in magnitude units, the residual difference is shown. Thesesmall residuals indicate that the indices are reproduced well by themodels.The equivalent figure for late types is presented in Fig. 12 with 7low to high mass bins going from green to blue. Generally thesegalaxies display weaker absorption than their ETG counterparts.Again, H 𝛽 increases with radius, and a clear mass-dependent ra-dial trend is seen, unlike ETGs, such that high mass galaxies havesteep radial gradients which flatten for low mass galaxies. Ongo-ing star formation in spiral galaxies leads to larger H 𝛽 values, sincethis index anti-correlates with age. Most of the features display verysmooth profiles, with striking radial gradients and a clear distinctionbetween the different mass bins. The transition from low to highmasses is smoother for LTGs, than for ETGs. The residuals betweenthe models and data are larger compared to ETGs. While the residualsscatter around zero in most cases, H 𝛽 and < Fe > are always slightlyunder-predicted by the models.The lowest mass bin shows the strongest H 𝛽 absorption but alsowith the largest uncertainties on this measurement, because this spec-tral region containing emission lines has a large associated error.Since all our parameters depend on the H 𝛽 equivalent width, weexclude this mass bin from the rest of our analysis.As mentioned before, we have updated our error estimation sothat we take the standard deviation of all individual spectra fromeach galaxy contributing to a mass bin, rather than the standarddeviation of the radially stacked spectra from galaxies. It can be seenin Fig. 11 and 12 that the biggest effect on the index errors is thatthey are smaller than our previous errors close to 1R 𝑒 , while errorsin galaxy centres continue to remain negligible. Since the numberof spectra contributing to the stack increases as a function of radius,our improved method makes a difference to the errors at large radii.In the centre, the radial bins cover small areas and hence the numberof galaxies is similar to the total number of spectra. In the next series of plots, we present the parameters correspondingto the best-fit models shown in Fig. 11 and 12. ETGs are shown onthe left and LTGs on the right. The scale on the y-axis is kept thesame for each parameter to allow a direct visual comparison.The symbols show the derived parameter for each stacked spectrum
MNRAS000
MNRAS000 , 1–30 (2021) patially-resolved stellar population properties Figure 11.
Absorption index measurements from stacked spectra of ETGs are shown as a function of radius as coloured circles, with Monte-Carlo based 1- 𝜎 errors. Yellow to red represents increasing galaxy mass in six bins. Best fitting stellar population models obtained at each point are shown as dashed lines. Thebottom panels show the residuals between the data and the models divided by the dynamical range of each index, except for CN1 which is measured in magnitudeunits. with 1- 𝜎 error-bars. These errors are calculated using a Monte Carloapproach, such that index measurements are randomly perturbed bytheir errors 100 times and stellar population parameters are derivedeach time by fitting models. The standard deviation between theparameters derived during each realisation is used to determine thefinal error on the parameter. Hence these are statistical errors basedon the S/N of the stacked spectra. Errors are generally smaller thansymbol sizes. The typical error in age is 0.5 Gyr, increasing to 1 Gyrfor the lowest mass LTGs, and the typical error in total metallicity andabundances is 0.03 dex. We note that further uncertainty might beintroduced for the LTGs due to fitting with SSP models (Section 2.4),depending on the star formation histories of these galaxies.The radial gradient and its error on each parameter for the differ-ent galaxy masses are given in Tables 2 & 3, and a comparison ofthe radial gradients with galaxy mass and type, as well as with P19,follows after the results for all parameters are presented. Radial gra-dients are calculated from a linear fit (in log-linear scale) to the pointsand errors on the gradients are calculated based on the deviation ofpoints from the fit. Since the scatter is greater than the errors on theparameters, the former dominates. We provide the gradient within 1R 𝑒 and between 1-1.5 R 𝑒 separately, because we notice differencesat larger radii. None of the calculated parameters of age, metallicity, and [Mg/Fe]hit the edges of the model grids except for three regions in the highestmass galaxies for which we derive the maximum age of 13.7 Gyrs.For the other abundances [X/Mg], the lower limit of -0.3 dex isreached for LTGs in some instances: low mass galaxies for Na, Ca,Ti, and most galaxies for N. Thus when we perturb the models by ± Fig. 13 shows the the stellar population age (top panel) and metal-licity (bottom panel), with the colour scheme as before indicatinggalaxy type, and within these types, different mass bins. The topleft panel shows that more massive ellipticals and lenticulars above11 log 𝑀 / 𝑀 (cid:12) are older, at ∼
13 Gyr, and the age steadily decreasesas galaxies become less massive, reaching ∼ . − . 𝑀 / 𝑀 (cid:12) . The gradients in age are negligi-bly small. These galaxies have negative metallicity gradients withslightly super-solar metallicities in centres, and sub-solar metallici-ties at large radii. The metallicity gradients, shown in the bottom leftpanel are slightly shallow for low mass galaxies, − . ± .
03, and
MNRAS , 1–30 (2021) T. Parikh et al.
Figure 12.
Same as Fig. 11 for late types. There are seven galaxy mass bins, with green shades representing lower mass and blue shades representing highermass galaxies. steepen, − . ± .
03 dex/R 𝑒 , for the more massive galaxies. Our re-sults of flat age gradients and negative metallicity gradients in ETGsare qualitatively in agreement with previous results (Kobayashi &Arimoto 1999; Mehlert et al. 2000; Mehlert et al. 2003; Kuntschner2004a; Spolaor et al. 2010; Greene et al. 2015; González Delgadoet al. 2015; Goddard et al. 2017; Zibetti et al. 2019). Comparing thegradients in detail, our age gradients for intermediate-mass galaxiesare slightly steeper at − . ± .
05 compared to − . ± .
03 dex/R 𝑒 from Goddard et al. (2017). However our low and high mass galax-ies are consistent with their results of negligible gradients. Ourmetallicity gradients are also slightly steeper than their reported − . ± .
03 dex/R 𝑒 for the highest mass galaxies.For spiral galaxies we show both LW and MW ages. These ageshave been derived by correcting for biases due to modelling with SSPsas described in Section 2.4. Note that these corrections have beenderived for solar metallicity and abundance CSPs. Spiral galaxiesdisplay younger ages between 2 - 6 Gyrs, and only the innermostregions of galaxies more massive than 10 . 𝑀 / 𝑀 (cid:12) are as old asETGs with ages of ∼
10 Gyrs. These central ages are slightly olderthan previously reported for bulges (e.g. Proctor & Sansom 2002;Thomas & Davies 2006), between 1.3 - 6 Gyrs.LTGs have negative age gradients, as expected from the H 𝛽 profilesseen in Fig. 12. These gradients clearly represent the transition frombulge to disk regions. Galaxies with masses >10 . 𝑀 / 𝑀 (cid:12) have very steep age gradients of − . ± .
04 on average within the half-light radius. Goddard et al. (2017) also find negative radial gradientsfor age and metallicity in LTGs however interestingly, they do notfind age gradients steepening with galaxy mass like us. Their studyuses full spectrum fitting with linear combinations of SSPs to derivestellar population parameters, hence we expect to find differencesdue to the vastly varied methodology, but it is encouraging to seesome agreement.In the outer regions the age gradients reverse to 0 . ± .
19 onaverage for the massive galaxies. This can also be seen for the inter-mediate and low mass galaxies. This reversal in age gradients beyond1 R 𝑒 is very interesting and could be due to radial migration. Zhenget al. (2017) derived luminosity-weighted ages for MaNGA galaxiesusing full spectrum fitting that showed a minimum near the half-lightradius and increased with radius beyond this.The correction for MW ages is larger than for LW ages (from Sec-tion 2.4), hence these ages are older. They range from 3.5 - 7.5 Gyrs,with the central regions of massive galaxies reaching 12.5 Gyrs. Thegradients in MW age are negative but shallower and display a weakerreversal beyond the half-light radius than the LW ages. Boardmanet al. (2020) find similar negative age gradients in their sample ofMilky Way-like galaxies from the MaNGA survey. Goddard et al.(2017) also find older MW ages but flat radial gradients, suggestingthat the inside-out formation signature is weak and the overall mass MNRAS000
19 onaverage for the massive galaxies. This can also be seen for the inter-mediate and low mass galaxies. This reversal in age gradients beyond1 R 𝑒 is very interesting and could be due to radial migration. Zhenget al. (2017) derived luminosity-weighted ages for MaNGA galaxiesusing full spectrum fitting that showed a minimum near the half-lightradius and increased with radius beyond this.The correction for MW ages is larger than for LW ages (from Sec-tion 2.4), hence these ages are older. They range from 3.5 - 7.5 Gyrs,with the central regions of massive galaxies reaching 12.5 Gyrs. Thegradients in MW age are negative but shallower and display a weakerreversal beyond the half-light radius than the LW ages. Boardmanet al. (2020) find similar negative age gradients in their sample ofMilky Way-like galaxies from the MaNGA survey. Goddard et al.(2017) also find older MW ages but flat radial gradients, suggestingthat the inside-out formation signature is weak and the overall mass MNRAS000 , 1–30 (2021) patially-resolved stellar population properties Figure 13.
The ages and metallicities as a function of radius are shown for ETGs (left) and LTGs (centre and right). For LTGs, both LW and MW ages areshown. The colours represent different mass bins, as before. The symbols are the derived parameters for each stacked spectrum and are connected by lines. 1- 𝜎 error bars on the parameters, based on 100 realisations using the index errors, are shown. budget is little affected. We note that although our methodology isnot expected to provide MW quantities, we use the correction derivedfrom Section 2.4 to calculate these ages, which provide an indicationof how LW and MW parameters might differ. In all following plotswe show LW ages for LTGs.LTGs are more metal poor than ETGs. The lowest mass spirals arevery metal poor with values between -0.5 and -1.0 dex (a tenth of solarmetallicity). All galaxies have negative metallicity gradients, withthe average across all masses being − . ± .
02 dex/R 𝑒 . Goddardet al. (2017) find gradients ranging between 0 . ± .
01 to − . ± .
06 dex/R 𝑒 . Negative radial gradients have also been obtained inthe Milky Way (Carollo et al. 2007; Hayden et al. 2015), and for diskgalaxies from the CALIFA survey (Sánchez-Blázquez et al. 2014;González Delgado et al. 2015).Peletier et al. (2007); Ganda et al. (2007) measure 2 dimensionalabsorption features (H 𝛽 , Fe5015, Mgb) for a small sample of spiralgalaxies and translate these to age, metallicity, and [Mg/Fe]. Theyfind younger ages, lower metallicities and abundances close to solarfor spirals compared to ETGs. Scott et al. (2017) report the sameusing the SAMI survey. We now present the derived element abundances for C, N, Na, Mg,Ca, and Ti. See P19 and references therein for a detailed comparisonwith the literature and implications of element abundances. Here,we will focus on describing the extension in mass and radius forETGs, any differences compared to P19, and the results for LTGs.Abundances of individual elements in these galaxy types have beenstudied in a very limited sense in literature before. However, thereare some studies which look at a single [ 𝛼 /Fe] parameter, which wecompare with in Section 4.C and Mg abundances are shown in Fig. 14. [C/Fe] values for Figure 14.
Same as Fig. 13 for C and Mg abundances.
ETGs range between 0.2 to 0.4 dex for different masses. There isa clear trend of [C/Fe] enhancement with increasing galaxy mass.For spiral galaxies, these abundances range between 0.1 to 0.3 dex,ignoring values with very large errors. For these galaxies, the trendwith mass is not as evident and there is more scatter. Both typesdisplay negligible gradients with radius, and the values inside andoutside 1 R 𝑒 are generally consistent.The behaviour of [Mg/Fe] for ETGs and LTGs is very similar toC: it has similar abundances and little variation with radius. Thisis consistent with previous studies for ETGs (Mehlert et al. 2003;Kuntschner 2004b; Johansson et al. 2012; Alton et al. 2018). How-ever, beyond 1 R 𝑒 , the gradients become more positive for all galaxytypes and masses. This effect is stronger for lower masses and hence,for LTGs this causes a reversed trend with mass such that more MNRAS , 1–30 (2021) T. Parikh et al.
Figure 15.
Same as Fig. 13 for Ca and Ti abundances. massive galaxies are less enhanced. This is potentially interestingin determining accretion histories. Greene et al. (2013) find radiallyconstant large [Mg/Fe] ratios out to the haloes of elliptical galaxies,and rising ratios for lower dispersion galaxies. Thomas et al. (1999)suggest that large [Mg/Fe] ratios at large radii, in relatively metal-poor regions, show evidence for a fast clumpy collapse model forthe formation of massive ellipticals, rather than a merging spiralsscenario which would result in solar [Mg/Fe] values in the very outerregions (Coccato et al. 2010).Next we move on to the heavier 𝛼 elements, Ca, and Ti, shownin Fig. 15. As found before in P19 for ETGs, Ca does not followMg, and instead is under-enhanced, with values between 0 and 0.2dex, and no variation with radius. Ca under-abundance in ETGs,also previously reported by (Saglia et al. 2002; Cenarro et al. 2003;Thomas et al. 2003b; Graves et al. 2007; Smith et al. 2009; Priceet al. 2011), was interpreted as contribution to Ca from delayed TypeIa supernovae. Spiral galaxies are even further depleted in Ca, withsub-solar abundances between -0.4 and 0 dex. Low mass spirals areexpected to be enriched in both Ca and Fe through their extendedstar formation history hence these low [Ca/Fe] ratios suggest that Caunder-abundance cannot be a result of short star formation timescales.LTGs interestingly display negative radial gradients within the half-light radius, as steep as − . ± .
10 for some of the intermediatemass spirals. The regions with low [Ca/Fe] ratios are very metal poorhence metallicity could be causing Ca under-abundance in LTGs.Ti values lie somewhere between Ca and Mg, with values between0 and 0.3 dex for ETGs, similar to (J12; P19), and between -0.3 and0.2 dex for LTGs, neglecting outliers with large errors. Interestingly,for LTGs, Ti abundances not as low as Ca, which fits in to thepicture that low Ca cannot be caused by delayed Type Ia enrichment.The radial gradients are also similar to Ca, with ETGs showing novariation, and LTGs displaying negative radial gradients. LTGs alsoshow varying gradients in the inner and outer regions, without anyclear trends, making the results unreliable and difficult to interpret.Lastly, Fig. 16 shows N and Na abundances, the two elementsfor which we detected negative radial gradients in P19. Looking at[N/Fe], we find abundance values ranging from -0.15 to 0.3 dexfrom low to high mass ETGs. We now see shallow radial gradientscompared to P19, based on a more robust analysis out to largerradii. For the same mass range, we previously derived − . ± . − . ± .
05 dex/R 𝑒 , and now find 0 . ± .
06, and − . ± .
03 dex/R 𝑒 , which are inconsistent to 2-3 𝜎 . The present results Figure 16.
Same as Fig. 13 for N and Na abundances. are more trustworthy and cause us to revise our conclusion regarding[N/Fe] gradients. Spiral galaxies have a scattered trend of [N/Fe] withmass and display negligible radial gradients, except for the highestmass bin.As before, we find high [Na/Fe] in ETGs, with the more massiveones reaching up to 0.6 dex. In P19, the highest mass bin showed 0.5dex [Na/Fe] in the centre, and in this work we have two mass binshigher than this. We see strong radial gradients, which are steeperfor more massive galaxies. The gradients range from − . ± . − . ± .
02 dex/R 𝑒 . These gradients within the effective radiusare consistent with the gradients from P19 of − . ± .
03 and − . ± .
02. Beyond 1 R 𝑒 [Na/Fe] seems to flatten. [N/Fe] alsoflattens (or even rises for LTGs), similar to [Mg/Fe].We note that there is a sharp jump in Na abundance for the lowestmass bin which cannot be physical. This is caused by the suddenchange in NaD absorption (see Fig. 11), and the fact that [Na/Fe]is highly sensitive to changes in this feature. Hence we will notconsider this mass bin in plots when measuring the [Na/Fe] gradientwith radius or velocity dispersion. Low mass spiral galaxies are lessenhanced in Na, with values between -0.25 and 0.25 dex, and highmass spirals are enhanced to twice solar Na abundances in the centres.These galaxies also show negative radial gradients in Na. It is veryinteresting to find the same radial behaviour in Na for LTGs. Fig. 17 provides a comparison of the radial gradients within 1 R 𝑒 in each parameter, for the different galaxy types. The value of theradial gradient and its error is shown as a function of mass, wherethe mass is the average of all galaxies in that mass bin. ETGs areshown as circles and LTGs are shown as squares; each mass bin isa different colour. The overlap in mass allows us to compare trendsfor different galaxy types at the same mass to determine whetherstellar populations vary depending on morphology. Linear fits, with 𝜎 -clipping to exclude outliers, are shown for both galaxy types, andthe corresponding slopes are given in Table 4. Also shown hereare our results from P19, as grey circles with error bars. These areconsistent with our present ETG results for all parameters except[N/Fe].For galaxies with log M/M (cid:12) <10, stellar population age gradientsare small and do not depend on the type of galaxy. More massivegalaxies however show a very clear divergence: for increasing galaxy MNRAS000
02. Beyond 1 R 𝑒 [Na/Fe] seems to flatten. [N/Fe] alsoflattens (or even rises for LTGs), similar to [Mg/Fe].We note that there is a sharp jump in Na abundance for the lowestmass bin which cannot be physical. This is caused by the suddenchange in NaD absorption (see Fig. 11), and the fact that [Na/Fe]is highly sensitive to changes in this feature. Hence we will notconsider this mass bin in plots when measuring the [Na/Fe] gradientwith radius or velocity dispersion. Low mass spiral galaxies are lessenhanced in Na, with values between -0.25 and 0.25 dex, and highmass spirals are enhanced to twice solar Na abundances in the centres.These galaxies also show negative radial gradients in Na. It is veryinteresting to find the same radial behaviour in Na for LTGs. Fig. 17 provides a comparison of the radial gradients within 1 R 𝑒 in each parameter, for the different galaxy types. The value of theradial gradient and its error is shown as a function of mass, wherethe mass is the average of all galaxies in that mass bin. ETGs areshown as circles and LTGs are shown as squares; each mass bin isa different colour. The overlap in mass allows us to compare trendsfor different galaxy types at the same mass to determine whetherstellar populations vary depending on morphology. Linear fits, with 𝜎 -clipping to exclude outliers, are shown for both galaxy types, andthe corresponding slopes are given in Table 4. Also shown hereare our results from P19, as grey circles with error bars. These areconsistent with our present ETG results for all parameters except[N/Fe].For galaxies with log M/M (cid:12) <10, stellar population age gradientsare small and do not depend on the type of galaxy. More massivegalaxies however show a very clear divergence: for increasing galaxy MNRAS000 , 1–30 (2021) patially-resolved stellar population properties Table 2.
Radial gradients of stellar population parameters for ETGs in dex/ 𝑅 𝑒 ; age is in log Gyr/ 𝑅 𝑒 . The top rows for each parameter provide the gradientwithin 1 R 𝑒 , and the bottom rows provide the gradient between 1 and 1.5 R 𝑒 . Mass bin Age [Z/H] [C/Fe] [N/Fe] [Na/Fe] [Mg/Fe] [Ca/Fe] [Ti/Fe]8 . − . − . ± . − . ± . − . ± .
03 0 . ± .
05 0 . ± .
14 0 . ± . − . ± .
05 0 . ± . . ± . − . ± .
06 0 . ± .
09 0 . ± .
07 0 . ± .
11 0 . ± .
07 0 . ± .
13 0 . ± . . − . − . ± . − . ± .
03 0 . ± . − . ± . − . ± .
03 0 . ± .
02 0 . ± .
04 0 . ± . . ± . − . ± .
02 0 . ± . − . ± . − . ± .
30 0 . ± . − . ± .
01 0 . ± . . − . − . ± . − . ± . − . ± .
03 0 . ± . − . ± .
03 0 . ± .
02 0 . ± .
04 0 . ± . . ± . − . ± .
05 0 . ± .
04 0 . ± . − . ± . − . ± .
03 0 . ± . − . ± . . − . − . ± . − . ± . − . ± .
01 0 . ± . − . ± .
03 0 . ± .
02 0 . ± . − . ± . . ± . − . ± .
09 0 . ± . − . ± . − . ± . − . ± .
03 0 . ± .
06 0 . ± . . − . − . ± . − . ± . − . ± . − . ± . − . ± .
01 0 . ± .
02 0 . ± . − . ± . . ± . − . ± . . ± .
13 0 . ± .
23 0 . ± .
05 0 . ± .
03 0 . ± .
06 0 . ± . . − . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . . ± . − . ± .
06 0 . ± .
04 0 . ± .
02 0 . ± .
17 0 . ± .
08 0 . ± .
04 0 . ± . Table 3.
Same as Table 2 for LTGs.
Mass bin Age [Z/H] [C/Fe] [N/Fe] [Na/Fe] [Mg/Fe] [Ca/Fe] [Ti/Fe]9 . − . − . ± . − . ± . − . ± .
05 0 . ± . − . ± . − . ± . − . ± .
05 0 . ± . . ± . − . ± .
04 0 . ± .
11 0 . ± .
01 0 . ± .
06 0 . ± .
07 0 . ± .
07 0 . ± . . − . . ± . − . ± . − . ± . − . ± . − . ± .
05 0 . ± . − . ± . − . ± . . ± . − . ± .
27 0 . ± . − . ± .
26 0 . ± . − . ± .
26 0 . ± .
17 0 . ± . . − . − . ± . − . ± . − . ± . − . ± . − . ± .
11 0 . ± . − . ± . − . ± . . ± . − . ± .
18 0 . ± .
11 0 . ± . − . ± .
11 0 . ± .
01 0 . ± . − . ± . . − . − . ± . − . ± . − . ± . − . ± . − . ± .
07 0 . ± . − . ± . − . ± . . ± . − . ± .
10 0 . ± . − . ± .
01 0 . ± . − . ± .
05 0 . ± .
32 0 . ± . . − . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . . ± . − . ± .
08 0 . ± .
03 0 . ± .
23 0 . ± .
05 0 . ± .
01 0 . ± . − . ± . . − . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± . . ± . − . ± .
18 0 . ± .
15 0 . ± . − . ± .
07 0 . ± . − . ± .
52 0 . ± . mass, ETGs show negligible gradients, while LTG gradients arenegative. This is clearly shown by the flat red linear fit and thesteep blue linear fit, which cross over at low masses. For metallicity,while ETG gradients become increasingly negative for more massivegalaxies, LTGs have the same negative gradient regardless of mass.Metallicity gradients steepening for more massive ETGs is consistentwith other studies from the MaNGA survey, Oyarzún et al. (2019)(after noting that they probe larger radii) and Lacerna et al. (2020)For C and Mg, at low masses the radial gradients are slightly pos-itive, and they become shallower or slightly negative as galaxy massincreases. The gradients are independent of the type of galaxy. Forthe heavier elements, ETGs display similar gradients at all masses for[Ca/Fe], and become slightly negative as mass increases for [Ti/Fe].High mass spirals show larger variation in gradients with mass. Theoverall trend for Ca is unclear, but for Ti, the gradients become morenegative as galaxy mass increases, like ETGs.N gradients go from slightly positive or shallow at low masses, tonegative at high masses for both types. Finally, for Na, ETGs displaya slight steepening in the gradient for the most massive galaxies whilegradients are consistent for both types at intermediate masses. This suggests that the mass or some other property drives Na abundancevariation rather than galaxy type.Briefly, we find several interesting features while studying theseradial gradients in these parameters for different galaxy types. Themost striking difference is in the age gradients. While ETGs displayrelatively flat age gradients, at all masses, LTGs show negative agegradients, which become steeper with mass. Metallicity behaves ex-actly the opposite way, with negative gradients steepening with massfor ETGs and negative gradients overall for LTGs. These propertiescould provide clues into how these different galaxy types and theirstellar populations assembled and evolved. C and Mg abundancesand radial gradients are remarkably similar for both types, suggest-ing that star formation timescales are independent of galaxy type butrather depend on some other physical property i.e. galaxy mass orvelocity dispersion. Na behaves in the same way as metallicity, show-ing negative gradients for LTGs, and negative gradients that steepenwith mass for ETGs. We explore relations with velocity dispersionin the following section. MNRAS , 1–30 (2021) T. Parikh et al.
Figure 17.
For each parameter, the radial gradient and error within 1 R 𝑒 is shown as a function of galaxy stellar mass. Square symbols show LTGs and circlesshow ETGs, with the colours represent different galaxy mass bins. 𝜎 -clipped linear fits to both galaxy types are shown. The grey symbols are the results forETGs from P19. Table 4.
Slopes of the mass-gradient relations for the galaxy types i.e. gradients of the linear fits from Fig. 17
Galaxy type Age [Z/H] [C/Fe] [N/Fe] [Na/Fe] [Mg/Fe] [Ca/Fe] [Ti/Fe]ETGs − . ± . − . ± . − . ± . − . ± . − . ± .
05 0 . ± . − . ± . − . ± . − . ± . − . ± . − . ± . − . ± .
02 0 . ± . − . ± . − . ± . − . ± . Along with radial gradients, it is informative to look at trends withvelocity dispersion. We plot the spatially-resolved parameters forboth galaxy types, in different mass bins, so that we can identify localdeviations (radial) from the global relation with velocity dispersion.The increased sample size and inclusion of different morphologiesconsiderably expands our parameter space compared to P19, Section3.3.Fig. 18 shows all our derived stellar population parameters againstthe velocity dispersion. These velocity dispersion profiles shown inFig. 5 have been calculated by combining the dispersions from theindividual spectra belonging to each bin. The symbols represent 3key pieces of information: i) ETGs are shown as circles and LTGsare shown as squares, ii) different colours within each type repre- sent different mass bins, and iii) each symbol is one radial bin withdecreasing symbol size represents increasing radius from the centre,0-0.1 R 𝑒 , to the outermost bin, 1.3-1.5 R 𝑒 .We also plot linear fits to all ETG results in red, and to LTGsin blue. Deviations from this line suggest internal gradients withingalaxies are different from the overall global relation with 𝜎 . ForLTGs, the lowest mass bin in this plot is always offset from the otherpoints, causing any fit to be misleading, hence we do not includethese points in the linear fit. Lastly, the relation from J12 is shown asa dashed black line for comparison.Starting with stellar population age, ETGs are older with increas-ing velocity dispersion. This effect is owing to higher mass galaxiesbeing older and not due to radial gradients, since the points withineach mass bin lie flat or parallel to the global relation (solid red line).LTGs show a steeper increase than ETGs, and also have steep local MNRAS000
02 0 . ± . − . ± . − . ± . − . ± . Along with radial gradients, it is informative to look at trends withvelocity dispersion. We plot the spatially-resolved parameters forboth galaxy types, in different mass bins, so that we can identify localdeviations (radial) from the global relation with velocity dispersion.The increased sample size and inclusion of different morphologiesconsiderably expands our parameter space compared to P19, Section3.3.Fig. 18 shows all our derived stellar population parameters againstthe velocity dispersion. These velocity dispersion profiles shown inFig. 5 have been calculated by combining the dispersions from theindividual spectra belonging to each bin. The symbols represent 3key pieces of information: i) ETGs are shown as circles and LTGsare shown as squares, ii) different colours within each type repre- sent different mass bins, and iii) each symbol is one radial bin withdecreasing symbol size represents increasing radius from the centre,0-0.1 R 𝑒 , to the outermost bin, 1.3-1.5 R 𝑒 .We also plot linear fits to all ETG results in red, and to LTGsin blue. Deviations from this line suggest internal gradients withingalaxies are different from the overall global relation with 𝜎 . ForLTGs, the lowest mass bin in this plot is always offset from the otherpoints, causing any fit to be misleading, hence we do not includethese points in the linear fit. Lastly, the relation from J12 is shown asa dashed black line for comparison.Starting with stellar population age, ETGs are older with increas-ing velocity dispersion. This effect is owing to higher mass galaxiesbeing older and not due to radial gradients, since the points withineach mass bin lie flat or parallel to the global relation (solid red line).LTGs show a steeper increase than ETGs, and also have steep local MNRAS000 , 1–30 (2021) patially-resolved stellar population properties Figure 18.
Stellar population parameters are shown against the local velocity dispersion, for ETGs (circles) and LTGs (squares). The colours represent differentgalaxy mass bins, and decreasing symbol size represents increasing radius from the central radial bin, 0-0.1 R 𝑒 , to the outermost radial bin, 1.3-1.5 R 𝑒 . Linearfits (in log-log space) to all ETGs and LTGs are shown as red and blue solid lines, respectively. The lowest mass LTG bin is always offset, skewing the relation,and hence is not included in the fit. The relation from J12 is shown as a dashed black line for reference. age gradients since the data points within galaxies do not lie alongthe global relation (solid blue line). At high 𝜎 ETGs and LTGs havesimilar ages. This resembles the colour-magnitude diagram for galax-ies in terms of a blue cloud and red sequence. For the metallicity,LTGs again show a steeper increase with velocity dispersion globally.High mass ETGs have steep local metallicity gradients, which arevery evident in this figure, suggesting that internal processes withinthese galaxies are responsible for these steep gradients. These ageand metallicity trends with velocity dispersion for ETGs and LTGsare consistent with Li et al. (2018).[C/Fe] and [Mg/Fe] both increase at a similar rate with 𝜎 , inde-pendent of galaxy type although [Mg/Fe]- 𝜎 shows some scatter. Therelations within galaxies deviates only for the highest mass ETGswhich have a steep local [C/Fe] gradient. Since [Mg/Fe] is linkedwith formation timescales, this suggests that the formation time isthe same regardless of galaxy type or as a function of radius butonly depends on the velocity dispersion. This was also hinted at byFig. 17. (Greene et al. 2019) find [ 𝛼 /Fe] for ETGs to be correlatedwith velocity dispersion and stellar mass, and saturating at highermass.Thomas & Davies (2006) find that the relations of age, metallicity, and [ 𝛼 /Fe] with 𝜎 are the same for ETGs and LTGs. It is interestingthat with the added spatial information, we find differences betweenthe ages and metallicities in these galaxy types. Due to the negativeage and metallicity gradients within LTGs, both age and metallicityshow steeper relations with 𝜎 than ETGs. A recent result from theSAMI survey (Croom et al. 2012) obtained ETG ages, metallicities,[Mg/Fe], and [C/Fe], showing that the velocity dispersion is thedominating driver behind chemical gradients (Ferreras et al. 2019).They find, consistent with our results, that [Mg/Fe] gradients are lesssteep for more massive galaxies.We can see that for both [Ca/Fe] and [Ti/Fe], ETGs display a clearflat or shallow increase with 𝜎 . LTGs are a bit more complicated andappear to have steep relations with the velocity dispersion such thatlower 𝜎 galaxies have much lower [X/Fe] values.There is some local variation in the [N/Fe]- 𝜎 relation for ETGs,and LTGs show some scatter with 𝜎 . [Na/Fe] increases very steeplywith velocity dispersion. The different types seamlessly blend to-gether. It is very evident for the high mass ETGs that the localchange in Na abundance is much steeper than the overall trend, andnot at all for LTGs, very similar to the trends with metallicity. Due tothe steep [Na/Fe]- 𝜎 relation, high mass spirals have Na abundances MNRAS , 1–30 (2021) T. Parikh et al.
Figure 19.
Gradients of stellar population parameters with velocity dispersion are plotted as a function of the velocity dispersion. The global gradients acrossall galaxy masses are shown as shaded regions for ETGs (red) and LTGs (blue). The local gradients within galaxies for each mass bin are shown as circles forETGs and squares for LTGs. Error-bars and width of the shaded region represent ± 𝜎 errors. similar to their C and Mg abundances, while high mass ETGs havemuch larger Na abundances compared to the other elements.In all cases, the agreement with J12 is remarkably good, with asignificant difference seen only for metallicity. Their metallicities areconsistent with our results for the innermost radial bins, while themetallicity gradients within galaxies, which lead to lower values atlarge radii, cause these radial bins to be offset.To illustrate the trends seen in Fig. 18, Fig. 19 shows gradients ofstellar population parameters with velocity dispersion, as a functionof the velocity dispersion. This is similar to P19, Figure 5, withadditions in mass and type. For ETGs, the red shaded region is thegradient of each parameter with velocity dispersion for all galaxiesi.e. the slope of the solid red line from each panel in Fig. 18. Thecircles are the gradients with velocity dispersion within galaxies,with each galaxy mass bin represented by a different colour as before.These have been placed at the average 𝜎 for each bin and are shownwith error bars. The blue shaded regions and square symbols are thesame for LTGs. A positive gradient with 𝜎 means a negative radialgradient. Similar to the radial gradients, the errors on the gradientsof parameters with the velocity dispersion come from the deviationof points around the linear fit. The two lowest mass bins of LTGs are not shown since one has large errors due to H 𝛽 and the other has notbeen included in the linear fits in Fig. 18.The extension in mass compared to P19 confirms that for ETGs,the local age gradients (circles) are consistent with the global agegradient (red shaded region, 0.8 log Gyr/log km/s), and the localmetallicity gradients are steeper than the global relation (0.3 dex/logkm/s). The local metallicity gradients become steeper with increasingmass, with ∇ 𝜎 [Z/H] ∼ 𝜎 MNRAS000
Gradients of stellar population parameters with velocity dispersion are plotted as a function of the velocity dispersion. The global gradients acrossall galaxy masses are shown as shaded regions for ETGs (red) and LTGs (blue). The local gradients within galaxies for each mass bin are shown as circles forETGs and squares for LTGs. Error-bars and width of the shaded region represent ± 𝜎 errors. similar to their C and Mg abundances, while high mass ETGs havemuch larger Na abundances compared to the other elements.In all cases, the agreement with J12 is remarkably good, with asignificant difference seen only for metallicity. Their metallicities areconsistent with our results for the innermost radial bins, while themetallicity gradients within galaxies, which lead to lower values atlarge radii, cause these radial bins to be offset.To illustrate the trends seen in Fig. 18, Fig. 19 shows gradients ofstellar population parameters with velocity dispersion, as a functionof the velocity dispersion. This is similar to P19, Figure 5, withadditions in mass and type. For ETGs, the red shaded region is thegradient of each parameter with velocity dispersion for all galaxiesi.e. the slope of the solid red line from each panel in Fig. 18. Thecircles are the gradients with velocity dispersion within galaxies,with each galaxy mass bin represented by a different colour as before.These have been placed at the average 𝜎 for each bin and are shownwith error bars. The blue shaded regions and square symbols are thesame for LTGs. A positive gradient with 𝜎 means a negative radialgradient. Similar to the radial gradients, the errors on the gradientsof parameters with the velocity dispersion come from the deviationof points around the linear fit. The two lowest mass bins of LTGs are not shown since one has large errors due to H 𝛽 and the other has notbeen included in the linear fits in Fig. 18.The extension in mass compared to P19 confirms that for ETGs,the local age gradients (circles) are consistent with the global agegradient (red shaded region, 0.8 log Gyr/log km/s), and the localmetallicity gradients are steeper than the global relation (0.3 dex/logkm/s). The local metallicity gradients become steeper with increasingmass, with ∇ 𝜎 [Z/H] ∼ 𝜎 MNRAS000 , 1–30 (2021) patially-resolved stellar population properties gradients (symbols) are consistent with the global relations (shadedregions). These results are consistent with P19 for ETGs and we nowshow that the same is true for LTGs. For ETGs, [Mg/Fe] increasesslightly in the outer regions, causing the local 𝜎 gradients to benegative and systematically offset from the global relation.The global gradient of Ca with velocity dispersion is steeper forLTGs than for ETGs. Very low [Ca/Fe] ratios in LTGs at large radiilead to the steeper relation. The relations within galaxies are generallyconsistent with the global trend for each type. For Ti, the globalgradients of the galaxy types are consistent with each other.It appears that the most massive galaxies have slightly steeperlocal relations in N than the global relation for their respective types,as also found in P19 for ETGs, but the effect is weaker here anddisappears at low masses, particularly for LTGs. For Na, we expandupon our earlier picture and show that higher mass galaxies continueto have increasingly steep local abundance gradients, reaching 6dex/log km/s. LTGs display shallower ∇ 𝜎 [Na/Fe] seem to fit intothis picture because of their velocity dispersion, and hence it seemsthat galaxy type does not drive this relation. There is a significantdifference between the global and local relations only for the galaxieswith the largest velocity dispersions ( > We have derived ages, metallicities, and individual chemical abun-dances of 6 elements as a function of galaxy mass, velocity disper-sion, radius, and type. In this Section, we discuss what new insightsthese results offer and the implications of our results, particularly forthe different galaxy types.There were several changes to the sample selection and stackingmethod compared to P19, including a different morphological selec-tion, binned kinematic data, and a lower S/N criterion for individualspectra. It is therefore encouraging to find that our results for ETGsare generally consistent with what we found in P19. The addition oflower mass and higher mass ETGs has allowed us to extend and con-firm trends we spotted with our previously limited sample in mass.The increased S/N ratio at large radii, due to stacking more galax-ies in each mass bin and increasing the bin width, makes our radialgradients in this work more robust.
We study the correlations of all element abundances with metallicityin Fig. 20. The derived abundances are plotted as circles for ETGs andsquares for LTGs with error-bars, with colours representing differentmass bins. Each symbol is a different radial bin and decreasing circlesize showing increasing radius. The Spearman correlation coefficient, 𝜌 , is shown in each panel for ETGs and LTGs separately, and a linearfit is plotted when | 𝜌 | > . Our ETG abundances from P19 left some open questions. Here weexplore whether the new work sheds further light on these. We findthat LTGs have steep negative age gradients, and negative metallic-ity gradients. These gradients support inside-out formation of diskgalaxies (see discussion in Goddard et al. 2017). In spiral galaxieswith masses > . 𝑀 / 𝑀 (cid:12) , we see that the age flattens out, andeven increases in some cases, at large radii. This could be due tothe radial migration of slightly older stars moving outwards throughthe disk. Cook et al. (2016) measure stellar population gradients forETGs from the Illustris simulation and find that galaxies with largeaccreted fractions have shallower metallicity gradients and no effecton the age gradients in the halo. They estimate 2 - 4 R 𝑒 for the galaxyhalo. In the future, it would be interesting to use the secondary sam-ple from MaNGA, which reaches out to 2.5 R 𝑒 , in order to determineages and abundances in the inner halo.Solar [C/Mg] ratios places a lower limit on star formationtimescales (J12). Both [C/Fe] and [Mg/Fe] increase with velocitydispersion, independent of galaxy type and mass, and with shallowor negligible radial gradients. Hence, galaxies with higher velocitydispersions formed their stars earliest, and these timescales do notvary with radius. We now confirm the same across a larger parameterspace in galaxy mass, and LTGs also generally follow this trend.Although there are some interesting differences at large radii. Spiralsappear to show an increase in C and Mg abundances beyond 0.8R 𝑒 , with lower mass galaxies showing a greater increase such that atlarge radii, abundances are anti-correlated with mass. This is poten-tially very interesting and could be a result of complicated accretionhistories.We again find low [Ca/Fe] ratios, close to zero, with negligibleradial gradients for ETGs of all masses. The same is seen for spiralgalaxies, however the low mass galaxies have extremely sub-solar Caabundances of -0.5 dex. However, Ca4227 is the weakest among theabsorption features used in this work, hence it not be strong enoughfor accurate measurements for the low mass galaxies. Note also thatthis index is sensitive to both [C/Fe] and [N/Fe], bringing additionalsources of error.Since Ti was found to behave like the lighter elements C and Mg,we attributed this to the production of Ti in Type II supernovae.We find this to be the case again for all but the lowest mass ETGs.Puzzlingly, for LTGs we find that high mass galaxies have slightlysub-solar Ti abundances. It remains to be seen whether the strangeCa and Ti trends in spiral galaxies point to the difficulty in obtainingthese parameters.We previously reported radial gradients for [N/Fe], but the trend MNRAS , 1–30 (2021) T. Parikh et al.
Figure 20.
Correlations of element abundances with total metallicity. ETGs are shown as circles and LTGs are shown as squares. Different colours representdifferent mass bins, and symbol size decreases with increasing radius. The Spearman correlation coefficient, 𝜌 , is shown in each panel for ETGs and LTGsseparately. Linear fits are plotted for correlations >0.5. Figure 21.
Same as Fig. 20 for correlations with stellar population age.MNRAS000
Same as Fig. 20 for correlations with stellar population age.MNRAS000 , 1–30 (2021) patially-resolved stellar population properties with mass was unclear. Low mass galaxies had a positive gradient andhigher mass galaxies showed negative gradients. With our improvedstudy, we show that this weak signal disappears and we find flatgradients. As a result, N under-abundance is restricted to low massgalaxies, while high-mass galaxies have super-solar N abundances.Finally, for Na we find that the new sample extends the relationswith velocity dispersion and metallicity, such that as 𝜎 and [Z/H]decrease, Na abundances reach solar and sub-solar values, matchingMg abundances and eventually being less enhanced than Mg. Thisprovides further support for metallicity-dependent Na enrichment(Kobayashi et al. 2006). As mentioned in Section 2.2, we do not attempt to separate thecontributions from the bulge and the disk, of which the local 𝜎 isa combination of. Other works provide interesting clues regardingthese components, and our correlations with 𝜎 also motivate furtherinvestigations.Tabor et al. (2019) perform a spectroscopic bulge-disk decompo-sition of MaNGA ETGs and show that these components representdifferent stellar populations with distinct kinematics. They find thatgalaxy bulges have a lower stellar spin parameter, 𝜆 𝑅 (Emsellemet al. 2007), while disks tend towards higher values. In terms of thestellar populations, both bulge and disk regions are found to havesimilar ages, while the former are metal-rich than the latter. Thesestellar population results are consistent with Fraser-McKelvie et al.(2018) who separate bulge and disk regions in S0 galaxies.Furthermore, Cortese et al. (2016) find using SAMI data that kine-matically fast rotator ETGs and LTGs lie on a continuous kinematicplane defined by the spin and Sersic index. Analysing detailed stellarpopulations while dividing galaxies using kinematic classifications,as well as morphological, would help shed further light on this matter. In this work we investigated stellar population gradients in a largegalaxy sample of 895 ETGs and 1005 LTGs, ranging in stellarmass from 8 . − .
33 log 𝑀 / 𝑀 (cid:12) . We used a morphologicalclassification scheme based on a Deep Learning Catalog to separateearly and LTGs. We modified our stacking procedure, including theuse of wider radial bins in outer regions of galaxies to compensatefor the loss in surface brightness, to produce high quality spectra inmass and radial bins out to 1.5 Re. The LTG sample is cut basedon inclination in order to select mostly face-on galaxies, to reducethe effect due to ISM contamination of absorption features suchas NaD. We measured absorption features and determined best-fitstellar population models to derive ages, metallicities, and individualelement abundances for both galaxy types.Our main results are summarised below: • We find stark differences between the ages and metallici-ties of the two types. As well as recovering results from P19 forintermediate-mass ETGs, we show across a wider parameter spacethat ETGs have flat age and negative metallicity gradients where thelatter are steeper than the global relation with velocity dispersion.LTGs show negative age and negative metallicity gradients, pointingto inside-out formation scenarios. For these galaxies, it is the localage variation that is steep compared to the general trend. We observea reversal of the age gradient beyond the half-light radius. • For the abundances, we find C and Mg to be similarly enhancedin ETGs and LTGs, increasing as a function of velocity dispersion, in-dependent of radius. This implies constant star-formation timescalesat all locations in all galaxy types, which are only driven by thevelocity dispersion. • We find [Ca/Fe] to be less enhanced also in LTGs, with negativeradial gradients. The inference that Ti behaves similar to the lighter 𝛼 elements and therefore is produced mostly in Type II supernovae isnot clear since high-mass LTGs show much lower [Ti/Fe] values thanMg, although not as low as Ca. LTGs show flattening or even increasein slopes of [C/Fe], [Mg/Fe], and [N/Fe] beyond 1R 𝑒 , suggesting thatthe stellar populations in the outskirts of LTGs are different. • We confirm steep local Na gradients for ETGs, which steepenwith increasing galaxy mass, while LTGs also show radial gradientsin Na. • Looking at trends with the velocity dispersion for both galaxytypes, we find that differences arise due to the local age gradients,and very low abundances ratios for [Ca/Fe] and [Ti/Fe] in LTGs.The most striking differences between galaxy types are seen forthe age, metallicity, and Na abundance, providing clues regardingthe formation scenarios for these galaxies. Constraints on Ca and Timust be improved through updated stellar population models in orderto gain better insights. • Strong Na abundance gradients within galaxies suggest internalprocesses drive Na enrichment. [Na/Fe]-[Z/H] relations show that Naabundances increase with both of these properties, independent ofgalaxy type and provide support for metallicity-dependent supernovayields for this element. Na, as well as the other elements, also corre-late with age suggesting a combination of effects leading to high Naabundances.In the future, we plan to derive the low-mass IMF slope for thesegalaxies. Of particular interest are LTGs, for which the IMF has beenstudied to a very limited extent. The spectral features used in thiswork are not sensitive to the IMF, but the MaNGA wavelength rangemakes a several such features accessible.
ACKNOWLEDGEMENTS
MNRAS , 1–30 (2021) T. Parikh et al.
University, Kavli Institute for the Physics and Mathematics of theUniverse (IPMU) / University of Tokyo, Lawrence Berkeley NationalLaboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut fürExtraterrestrische Physik (MPE), National Astronomical Observato-ries of China, New Mexico State University, New York University,University of Notre Dame, Observatário Nacional / MCTI, The OhioState University, Pennsylvania State University, Shanghai Astronom-ical Observatory, United Kingdom Participation Group, UniversidadNacional Autónoma de México, University of Arizona, University ofColorado Boulder, University of Oxford, University of Portsmouth,University of Utah, University of Virginia, University of Washington,University of Wisconsin, Vanderbilt University, and Yale University.
DATA AVAILABILITY
REFERENCES
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APPENDIX A: EFFECT OF DIFFERENT METALLICITIESAND ALPHA ABUNDANCES
The effect of CSP models at different metallicities and abundancesis shown in Fig. A1, analogous to Fig. 8. The top panel shows [Z/H]= -0.3, 0.0, and +0.3 at fixed [ 𝛼 /Fe] = 0.0, while the bottom panelshows [ 𝛼 /Fe] = -0.3, 0.0, and +0.3 at fixed [Z/H] = 0.0. Tests areperformed as described in Section 2.4 for four different 𝜏 values. Foreach metallicity or abundance, a fit is performed to the results fromall the 𝜏 models. The orange lines in both panels correspond to theresults for solar metallicity and abundance shown in Fig. 8.Focussing on the effect of different metallicities first, we see for allparameters that biases are similar at larger fitted ages. At younger fit-ted ages, the LW Age is underestimated more for super-solar modelsand less for sub-solar models compared to solar metallicity. The dif-ference between these models is however comparable to the spreaddue to different 𝜏 s. Since our LTGs are most affected by the as-sumption of a singular SFH, and these galaxies are solar or moremetal-poor, we can expect the level of correction to be no more than0.2 dex. The middle panel shows that the metallicity for metal-poorpopulations could be underestimated for ages younger than 0.4 logGyrs. Lastly the difference between the true and fitted [Mg/Fe] re-mains negligible regardless of the metallicity. We conclude that agesmust be corrected using solar CSPs, while the effect on metallicitiesand abundances is small, with the caveat that for very young ages,metallicities might be underestimated by up to 0.2 dex.The lower panel shows that the biases for all parameters are almostidentical to the solar case, regardless of super- or sub-solar elementabundances. Hence applying the correction based on the solar modelis valid for our LTGs. MNRAS , 1–30 (2021) T. Parikh et al.
Figure A1.
LW Age, fitted metallicity and fitted [Mg/Fe] as a function of fitted age for different metallicities (top) and different abundances (bottom). The fittedages correspond better to LW ages of a composite population. At young ages, the metallicity is overestimated by up to +0.2 dex. The fitted [Mg/Fe] appears tobe offset from the expected solar value by +0.15 dex at young ages. This decreases for older populations. Red circles show the same for fitting SSPs. The orangecurves are polynomial fits to the CSPs.
APPENDIX B: INDEX MEASUREMENTS ANDPARAMETERS
We provide our index measurements for the optical indices CN1,Ca4227, Fe4531, C 𝛽 , Mg 𝑏 , Fe5270, Fe5335, and NaD forall radial and mass bins in Table B1 for ETGs and Table B2 for LTGs,at MILES resolution.Also provided are the following parameters at each radial bin: age,metallicity, and the individual element abundances for C, N, Na, Mg,Ca, and Ti, derived using the TMJ models, in Table B3 and Table B4for ETGs and LTGs respectively. MNRAS000
We provide our index measurements for the optical indices CN1,Ca4227, Fe4531, C 𝛽 , Mg 𝑏 , Fe5270, Fe5335, and NaD forall radial and mass bins in Table B1 for ETGs and Table B2 for LTGs,at MILES resolution.Also provided are the following parameters at each radial bin: age,metallicity, and the individual element abundances for C, N, Na, Mg,Ca, and Ti, derived using the TMJ models, in Table B3 and Table B4for ETGs and LTGs respectively. MNRAS000 , 1–30 (2021) patially-resolved stellar population properties T a b l e B : M ea s u r e d e qu i v a l e n t w i d t h s a t a ll r a d i a l b i n s fr o m − . 𝑅 e f o r ET G s . T h e m ea s u r e m e n t s h a v e b ee n c o rr ec t e d t o M I LE S r e s o l u ti on . . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
04 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb3 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb4 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb4 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS , 1–30 (2021) T. Parikh et al. T a b l e B : ( c on ti nu e d ) . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb4 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
04 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb4 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS000
04 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb4 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS000 , 1–30 (2021) patially-resolved stellar population properties T a b l e B : S a m ea s T a b l e B f o r LT G s . . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b3 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
13 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b3 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
06 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
06 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS , 1–30 (2021) T. Parikh et al. T a b l e B : ( c on ti nu e d ) . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb3 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b2 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb3 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb4 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS000
02 10 . − . 𝑀 (cid:12) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . C N . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . C a . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . C . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . H b1 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . M gb4 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . F e . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . N a D . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS000 , 1–30 (2021) patially-resolved stellar population properties T a b l e B : S t e ll a r popu l a ti onp a r a m e t e r s a nd e rr o r s d e r i v e du s i ng t h e T M J m od e l s a nd ac o m b i n a ti ono f op ti ca li nd i ce s f o r ET G s . l og A g e ( G y r) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
03 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
03 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ Z / H ]( s o l a r s ca l e d ) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
02 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . - . ± .
02 11 . − . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . [ C / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ N / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . . ± .
02 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . - . ± . . ± . . ± . . ± . . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ N a / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . . ± . . ± . . ± . . ± . . ± .
02 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ M g / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS , 1–30 (2021) T. Parikh et al. T a b l e B : ( c on ti nu e d ) [ C a / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ T i/ F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . - . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
04 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
02 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
03 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS000
03 11 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS000 , 1–30 (2021) patially-resolved stellar population properties T a b l e B : S a m ea s T a b l e B f o r LT G s . l og A g e 𝐿 𝑊 ( G y r) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
06 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
15 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
06 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
04 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ Z / H ]( s o l a r s ca l e d ) . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
05 9 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
11 9 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
03 10 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
02 10 . − . . ± . - . ± . - . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . [ C / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
03 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
05 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ N / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . . ± . . ± .
03 9 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
05 9 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . [ N a / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
03 9 . − . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
05 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . . ± . . ± . - . ± .
01 10 . − . . ± . . ± . - . ± . . ± . . ± . . ± . . ± . . ± . - . ± . . ± . - . ± .
02 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . [ M g / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
03 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
05 9 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . MNRAS , 1–30 (2021) T. Parikh et al. T a b l e B : ( c on ti nu e d ) [ C a / F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
03 9 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
05 9 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . - . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . - . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . . ± . - . ± . [ T i/ F e ] . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . - . . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± .
05 9 . − . . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . . ± .
07 9 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . - . ± . - . ± . - . ± .
01 10 . − . . ± . . ± . . ± . . ± . . ± . . ± . . ± . - . ± . - . ± . - . ± . - . ± . T h i s p a p e r h a s b ee n t yp e s e t fr o m a TE X / L A TE X fi l e p r e p a r e dby t h ea u t ho r . MNRAS000