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Abstract This is the third paper of a series where we study the stellar population gradients (SP; ages, metallicities, α-element abundance ratios and stellar initial mass functions) of early type galaxies (ETGs) at z ≤ 0.08 from the MaNGA-DR15 survey. In this work we focus on the S0 population and quantify how the SP varies across the population as well as with galactocentric distance. We do this by measuring Lick indices and comparing them to stellar population synthesis models. This requires spectra with high signal-to-noise which we achieve by stacking in bins of luminosity (Lr) and central velocity dispersion (σ0). We find that: 1) There is a bimodality in the S0 population: S0s more massive than 3 × 1010M⊙ show stronger velocity dispersion and age gradients (age and σr decrease outwards) but little or no metallicity gradient, while the less massive ones present relatively flat age and velocity dispersion profiles, but a significant metallicity gradient (i.e. [M/H] decreases outwards). Above 2 × 1011M⊙ the number of S0s drops sharply. These two mass scales are also where global scaling relations of ETGs change slope. 2) S0s have steeper velocity dispersion profiles than fast rotating elliptical galaxies (E-FRs) of the same luminosity and velocity dispersion. The kinematic profiles and stellar population gradients of E-FRs are both more similar to those of slow rotating ellipticals (E-SRs) than to S0s, suggesting that E-FRs are not simply S0s viewed face-on. 3) At fixed σ0, more luminous S0s and E-FRs are younger, more metal rich and less α-enhanced. Evidently for these galaxies, the usual statement that ‘massive galaxies are older’ is not true if σ0 is held fixed. 

Abstract We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs), and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97% accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼ 87% and 73% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ε) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.