arXiv subjects

M. Huertas-Company

Publications and source records attributed to M. Huertas-Company.

At least 55 records · Page 3Linked to original sources

arXiv2022

Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models

We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real Hubble Space Telescope observations by deep generative models. We simulate a galaxy field of $0.4\,\rm{deg}^2$ as it will be seen by the Euclid visible imager VIS and show that galaxy structural parameters are recovered with similar accuracy as for pure analytic Sérsic profiles. Based on these simulations, we estimate that the Euclid Wide Survey will be able to resolve the internal morphological structure of galaxies down to a surface brightness of $22.5\,\rm{mag}\,\rm{arcsec}^{-2}$, and $24.9\,\rm{mag}\,\rm{arcsec}^{-2}$ for the Euclid Deep Survey. This corresponds to approximately $250$ million galaxies at the end of the mission and a $50\,\%$ complete sample for stellar masses above $10^{10.6}\,\rm{M}_\odot$ (resp. $10^{9.6}\,\rm{M}_\odot$) at a redshift $z\sim0.5$ for the wide (resp. deep) survey. The approach presented in this work can contribute to improving the preparation of future high-precision cosmological imaging surveys by allowing simulations to incorporate more realistic galaxies.

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arXiv2021

The building up of observed stellar scaling relations of massive galaxies and the connection to black hole growth in the TNG50 simulation

[abridged] We study how mock-observed stellar morphological and structural properties of massive galaxies are built up between $z=0.5$ and $z=3$ in the TNG50 cosmological simulation. We generate mock images with the properties of the CANDELS survey and derive Sersic parameters and optical rest-frame morphologies as usually done in the observations. Overall, the simulation reproduces the observed evolution of the abundances of different galaxy morphological types of star-forming and quiescent galaxies. The $\log{M_*}-\log R_e$ and $\log{M_*}-\logΣ_1$ relations of the simulated star-forming and quenched galaxies also match the observed slopes and zeropoints to within 1-$σ$. In the simulation, galaxies increase their observed central stellar mass density ($Σ_1$) and transform in morphology from irregular/clumpy systems to normal Hubble-type systems in the Star Formation Main Sequence at a characteristic stellar mass of $\sim 10^{10.5}~M_\odot$. This morphological transformation is connected to the activity of the central Super Massive Black Holes (SMBHs). At low stellar masses ($10^9$ < $M_*/M_\odot$ < $10^{10}$) SMBHs grow rapidly, while at higher mass SMBHs switch into the kinetic feedback mode and grow more slowly. During this low-accretion phase, SMBH feedback leads to the quenching of star-formation, along with a simultaneous growth in $Σ_1$. More compact massive galaxies grow their SMBHs faster than extended ones of the same mass and end up quenching earlier. In the TNG50 simulation, SMBHs predominantly grow via gas accretion before galaxies quench, and $Σ_1$ increases substantially after SMBH growth slows down. The simulation predicts therefore that quiescent galaxies have higher $Σ_1$ values than star-forming galaxies for the same SMBH mass, which disagrees with alternative models, and may potentially be in tension with some observations.

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arXiv2021

SDSS-IV DR17: Final Release of MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs

We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from Sèrsic and Sèrsic+Exponential fits to the 2D surface brightness profiles of the MaNGA DR17 galaxy sample in the $g$, $r$, and $i$ bands (e.g. total fluxes, half light radii, bulge-disk fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides Deep Learning-based morphological classifications for the same galaxies. The MDLM-VAC-DR17 includes a number of morphological properties: e.g., a T-Type, a finer separation between elliptical and S0, as well as the identification of edge-on and barred galaxies. While the MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies which were added to make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements compared to the previous release (MDLM-VAC-DR15): namely, the low-end of the T-Types are better recovered in this new version. The catalogue also includes a separation between Early- or Late-type Galaxies (ETG, LTG), which classifies the two populations in a complementary way to the T-Type, especially at the intermediate types (-1 < T-Type < 2), where the T-Type values show a large scatter. In addition, $k-$fold based uncertainties on the classifications are also provided. To ensure robustness and reliability, we have also visually inspected all the images. We describe the content of the catalogues and show some interesting ways in which they can be combined.

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arXiv2021

The evolution of compact massive quiescent and starforming galaxies derived from the $R_e-R_h$ and $M_{\rm star}-M_h$ relations

The mean size ( effective radius $R_e$) of Massive Galaxies (MGs, $M_{\rm star}>10^{11.2}M_\odot$) is observed to increase steadily with cosmic time. It is still unclear whether this trend originates from the size growth of individual galaxies (via, e.g., mergers and/or AGN feedback) or from the inclusion of larger galaxies entering the selection at later epochs (progenitor bias). We here build a data-driven, flexible theoretical framework to probe the structural evolution of MGs. We assign galaxies to dark matter haloes via stellar mass-halo mass (SMHM) relations with varying high-mass slopes and scatters $σ_{\rm SMHM}$ in stellar mass at fixed halo mass, and assign sizes to galaxies using an empirically-motivated, constant and linear relationship between $R_e$ and the host dark matter halo radius $R_h$. We find that: 1) the fast mean size growth of MGs is well reproduced independently of the shape of the input SMHM relation; 2) the numbers of compact MGs grow steadily until $z\gtrsim2$ and fall off at lower redshifts, suggesting a lesser role of progenitor bias at later epochs; 3) a time-independent scatter $σ_{\rm SMHM}$ is consistent with a scenario in which compact starforming MGs transition into quiescent MGs in a few $10^8$yr with a negligible structural evolution during the compact phase, while a scatter increasing at high redshift implies significant size growth during the starforming phase. A robust measurement of the size function of MGs at high redshift can set strong constraints on the scatter of the SMHM relation and, by extension, on models of galaxy evolution.

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arXiv2021

A duality in the origin of bulges and spheroidal galaxies

Studying the resolved stellar populations of the different structural components which build massive galaxies directly unveils their assembly history. We aim at characterizing the stellar population properties of a representative sample of bulges and pure spheroids in massive galaxies ($M_{\star}>10^{10}$ M$_{\odot}$) in the GOODS-N field. We take advantage of the spectral and spatial information provided by SHARDS and HST data to perform the multi-image spectro-photometrical decoupling of the galaxy light. We derive the spectral energy distribution separately for bulges and disks in the redshift range $0.14<z\leq1$ with spectral resolution $R\sim50$. Analyzing these SEDs, we find evidences of a bimodal distribution of bulge formation redshifts. We find that 33% of them present old mass-weighted ages, implying a median formation redshift $z_{\rm{form}}={6.2}_{-1.7}^{+1.5}$. They are relics of the early Universe embedded in disk galaxies. A second wave, dominant in number, accounts for bulges formed at median redshift $z_{\rm{form}}={1.3}_{-0.6}^{+0.6}$. The oldest (1$^{\rm{st}}$-wave) bulges are more compact than the youngest. Virtually all pure spheroids (i.e., those without any disk) are coetaneous with the 2$^{\rm{nd}}$-wave bulges, presenting a median redshift of formation $z_{\rm{form}}={1.1}_{-0.3}^{+0.3}$. The two waves of bulge formation are not only distinguishable in terms of stellar ages, but also in star formation mode. All 1$^{\rm st}$-wave bulges formed fast at $z\sim6$, with typical timescales around 200 Myr. A significant fraction of the 2$^{\rm{nd}}$-wave bulges assembled more slowly, with star formation timescales as long as 1 Gyr. The results of this work suggest that the centers of massive disk-like galaxies actually harbor the oldest spheroids formed in the Universe.

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arXiv2021

Pushing automated morphological classifications to their limits with the Dark Energy Survey

We present morphological classifications of $\sim$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 $\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to $\mathrm{m}_r < 21.5$ 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 $\mathrm{m}_r<21.5$ 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 $\sim$ 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 (\textit{n}), ellipticity ($ε$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.

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arXiv2020

Stellar Masses of Giant Clumps in CANDELS and Simulated Galaxies Using Machine Learning

A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of a complete sample of $\sim1500$ star forming galaxies at $1<z<3$ in the CANDELS survey as well as in images from the VELA zoom-in cosmological simulations. We show that observational effects have a dramatic impact on the derived clump properties leading to an overestimation of the clump mass up to a factor of 10, which highlights the importance of fair comparisons between observations and simulations and the limitations of current HST data to study the resolved structure of distant galaxies. After correcting for these effects with a mixture density network, we estimate that the clump stellar mass function follows a power-law down to the completeness limit ($10^{7}$ solar masses) with the majority of the clumps being less massive than $10^9$ solar masses. This is in better agreement with recent gravitational lensing based measurements. The simulations explored in this work overall reproduce the shape of the observed clump stellar mass function and clumpy fractions when confronted under the same conditions, although they tend to lie in the lower limit of the confidence intervals of the observations. This agreement suggests that most of the observed clumps are formed in-situ.

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arXiv2020

Structural and Stellar Population Properties vs. Bulge Types in Sloan Digital Sky Survey Central Galaxies

This paper studies pseudo-bulges (P-bulges) and classical bulges (C-bulges) in Sloan Digital Sky Survey central galaxies using the new bulge indicator $ΔΣ_1$, which measures relative central stellar-mass surface density within 1 kpc. We compare $ΔΣ_1$ to the established bulge-type indicator $Δ\langleμ_e\rangle$ from Gadotti (2009) and show that classifying by $ΔΣ_1$ agrees well with $Δ\langleμ_e\rangle$. $ΔΣ_1$ requires no bulge-disk decomposition and can be measured on SDSS images out to $z = 0.07$. Bulge types using it are mapped onto twenty different structural and stellar-population properties for 12,000 SDSS central galaxies with masses 10.0 < log $M_*$/$M_{\odot}$ < 10.4. New trends emerge from this large sample. Structural parameters show fairly linear log-log relations vs. $ΔΣ_1$ and $Δ\langleμ_e\rangle$ with only moderate scatter, while stellar-population parameters show a highly non-linear "elbow" in which specific star-formation rate remains roughly flat with increasing central density and then falls rapidly at the elbow, where galaxies begin to quench. P-bulges occupy the low-density end of the horizontal arm of the elbow and are universally star-forming, while C-bulges occupy the elbow and the vertical branch and exhibit a wide range of star-formation rates at fixed density. The non-linear relation between central density and star-formation rate has been seen before, but this mapping onto bulge class is new. The wide range of star-formation rates in C-bulges helps to explain why bulge classifications using different parameters have sometimes disagreed in the past. The elbow-shaped relation between density and stellar indices suggests that central structure and stellar-populations evolve at different rates as galaxies begin to quench.

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arXiv2019

The Hubble Sequence at $z\sim0$ in the IllustrisTNG simulation with deep learning

We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $z\sim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete sample of $\sim12,000$ galaxies in the simulation using the radiative transfer code SKIRT and include PSF and noise to match the SDSS r-band properties. The images are then processed through the exact same neural network used to estimate SDSS morphologies to classify simulated galaxies in four morphological classes (E, S0/a, Sab, Scd). The CNN model finds that $\sim95\%$ of the simulated galaxies fall in one the four main classes with high confidence. The mass-size relations of the simulated galaxies divided by morphological type also reproduce well the slope and the normalization of observed relations which confirms the realism of optical morphologies in the TNG suite. However, the Stellar Mass Functions decomposed into different morphologies still show significant discrepancies with observations both at the low and high mass end. We find that the high mass end of the SMF is dominated in TNG by massive disk galaxies while early-type galaxies dominate in the observations according to the CNN classifications. The present work highlights the importance of detailed comparisons between observations and simulations in comparable conditions.

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arXiv2019

Transfer learning for galaxy morphology from one survey to another

Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.

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arXiv2018

The host galaxies of luminous type 2 AGN at $z \sim$0.3-0.4

We study the morphological and structural properties of the host galaxies associated with 57 optically-selected luminous type 2 AGN at $z\sim$0.3-0.4: 16 high-luminosity Seyfert 2 (HLSy2, 8.0$\le$log($L_{\rm [OIII]}/L_{\odot})<$8.3) and 41 obscured quasars (QSO2, log($L_{\rm [OIII]}/L_{\odot})\ge$8.3). With this work, the total number of QSO2 at $z<1$ with parametrized galaxies increases from $\sim$35 to 76. Our analysis is based on HST WFPC2 and ACS images that we fit with {\sc GALFIT}. HLSy2 and QSO2 show a wide diversity of galaxy hosts. The main difference lies in the higher incidence of highly-disturbed systems among QSO2. This is consistent with a scenario in which galaxy interactions are the dominant mechanism triggering nuclear activity at the highest AGN power. There is a strong dependence of galaxy properties with AGN power (assuming $L_ {\rm [OIII]}$ is an adequate proxy). The relative contribution of the spheroidal component to the total galaxy light (B/T) increases with $L_ {\rm [OIII]}$. While systems dominated by the spheoridal component spread across the total range of $L_ {\rm [OIII]}$, most disk-dominated galaxies concentrate at log($L_{\rm [OIII]}/L_{\odot})<$8.6. This is expected if more powerful AGN are powered by more massive black holes which are hosted by more massive bulges or spheroids. The average galaxy sizes ($\langle r_{\rm e} \rangle$) are 5.0$\pm$1.5 kpc for HLSy2 and 3.9$\pm$0.6 kpc for HLSy2 and QSO2 respectively. These are significantly smaller than those found for QSO1 and narrow line radio galaxies at similar $z$. We put the results of our work in context of related studies of AGN with quasar-like luminosities.

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arXiv2018

On the Transition of the Galaxy Quenching Mode at 0.5<z<1 in CANDELS

We investigate the galaxy quenching process at intermediate redshift using a sample of $\sim4400$ galaxies with $M_{\ast} > 10^{9}M_{\odot}$ between redshift 0.5 and 1.0 in all five CANDELS fields. We divide this sample, using the integrated specific star formation rate (sSFR), into four sub-groups: star-forming galaxies (SFGs) above and below the ridge of the star-forming main sequence (SFMS), transition galaxies and quiescent galaxies. We study their $UVI$ ($U-V$ versus $V-I$) color gradients to infer their sSFR gradients out to twice effective radii. We show that on average both star-forming and transition galaxies at all masses are not fully quenched at any radii, whereas quiescent galaxies are fully quenched at all radii. We find that at low masses ($M_{\ast} = 10^{9}-10^{10}M_{\odot}$) SFGs both above and below the SFMS ridge generally have flat sSFR profiles, whereas the transition galaxies at the same masses generally have sSFRs that are more suppressed in their outskirts. In contrast, at high masses ($M_{\ast} > 10^{10.5}M_{\odot}$), SFGs above and below the SFMS ridge and transition galaxies generally have varying degrees of more centrally-suppressed sSFRs relative to their outskirts. These findings indicate that at $z\sim~0.5-1.0$ the main galaxy quenching mode depends on its already formed stellar mass, exhibiting a transition from "the outside-in" at $M_{\ast} \leq 10^{10}M_{\odot}$ to "the inside-out" at $M_{\ast} > 10^{10.5}M_{\odot}$. In other words, our findings support that internal processes dominate the quenching of massive galaxies, whereas external processes dominate the quenching of low-mass galaxies.

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arXiv2018

Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a Convolutional Neural Network (CNN) with mock "observed" images of simulated galaxies at three phases of evolution: pre-BN, BN and post-BN, and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of $\sim0.15$ Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at $z=1-3$, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, $10^{9.2-10.3} M_\odot$ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations, and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations.

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arXiv2018

$M_*/L$ gradients driven by IMF variation: Large impact on dynamical stellar mass estimates

Within a galaxy the stellar mass-to-light ratio $Υ_*$ is not constant. Spatially resolved kinematics of nearby early-type galaxies suggest that allowing for a variable initial mass function (IMF) returns significantly larger $Υ_*$ gradients than if the IMF is held fixed. If $Υ_*$ is greater in the central regions, then ignoring the IMF-driven gradient can overestimate $M_*^{\rm dyn}$ by as much as a factor of two for the most massive galaxies, though stellar population estimates $M_*^{\rm SP}$ are also affected. Large $Υ_*$-gradients have four main consequences: First, $M_*^{\rm dyn}$ cannot be estimated independently of stellar population synthesis models. Second, if there is a lower limit to $Υ_*$ and gradients are unknown, then requiring $M_*^{\rm dyn}=M_*^{\rm SP}$ constrains them. Third, if gradients are stronger in more massive galaxies, then $M_*^{\rm dyn}$ and $M_*^{\rm SP}$ can be brought into agreement, not by shifting $M_*^{\rm SP}$ upwards by invoking constant bottom-heavy IMFs, as advocated by a number of recent studies, but by revising $M_*^{\rm dyn}$ estimates in the literature downwards. Fourth, accounting for $Υ_*$ gradients changes the high-mass slope of the stellar mass function $ϕ(M_*^{\rm dyn})$, and reduces the associated stellar mass density. These conclusions potentially impact estimates of the need for feedback and adiabatic contraction, so our results highlight the importance of measuring $Υ_*$ gradients in larger samples.

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arXiv2018

Improving galaxy morphologies for SDSS with Deep Learning

We present a morphological catalogue for $\sim$ 670,000 galaxies in the Sloan Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-Types and a series of GZ2 type questions (disk/features, edge-on galaxies, bar signature, bulge prominence, roundness and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-Type model is not so efficient. For the T-Type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (> 97\%), precision and recall values (> 90\%) when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.

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arXiv2017

Deep learning for galaxy surface brightness profile fitting

Numerous ongoing and future large area surveys (e.g. DES, EUCLID, LSST, WFIRST), will increase by several orders of magnitude the volume of data that can be exploited for galaxy morphology studies. The full potential of these surveys can only be unlocked with the development of automated, fast and reliable analysis methods. In this paper we present DeepLeGATo, a new method for two-dimensional photometric galaxy profile modeling, based on convolutional neural networks. Our code is trained and validated on analytic profiles (HST/CANDELS F160W filter) and it is able to retrieve the full set of parameters of one- component Sérsic models: total magnitude, effective radius, Sérsic index, axis ratio. We show detailed comparisons between our code and GALFIT. On simulated data, our method is more accurate than GALFIT and 3000 time faster on GPU (50 times when run on the same CPU). On real data, DeepLeGATo trained on simulations behaves similarly to GALFIT on isolated galaxies. With a fast domain adaptation step made with the 0.1 - 0.8 per cent the size of the training set, our code is easily capable to reproduce the results obtained with GALFIT even on crowded regions. DeepLeGATo does not require any human intervention beyond the training step, rendering it much automated than traditional profiling methods. The development of this method for more complex models (two-component galaxies, variable PSF, dense sky regions) could constitute a fundamental tool in the era of big data in astronomy.

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arXiv2017

Stellar mass functions and implications for a variable IMF

Spatially resolved kinematics of nearby galaxies has shown that the ratio of dynamical- to stellar population-based estimates of the mass of a galaxy ($M_*^{\rm JAM}/M_*$) correlates with $σ_e$, if $M_*$ is estimated using the same IMF for all galaxies and the stellar M/L ratio within each galaxy is constant. This correlation may indicate that, in fact, the IMF is more dwarf-rich for galaxies with large $σ$. We use this correlation to estimate a dynamical or IMF-corrected stellar mass, $M_*^{\rm α_{JAM}}$, from $M_{*}$ and $σ_e$ for a sample of $6 \times 10^5$ SDSS galaxies for which spatially resolved kinematics is not available. We also compute the `virial' mass estimate $k(n,R)\,R_e\,σ_R^2/G$, where $n$ is the Sersic index, in the SDSS and ATLAS$^{\rm 3D}$ samples. We show that an $n$-dependent correction must be applied to the $k(n,R)$ values provided by Prugniel & Simien (1997). Our analysis also shows that the shape of the velocity dispersion profile in the ATLAS$^{\rm 3D}$ sample varies weakly with $n$: $(σ_R/σ_e) = (R/R_e)^{-γ(n)}$. The resulting stellar mass functions, based on $M_*^{\rm α_{JAM}}$ and the recalibrated virial mass, are in good agreement. If the $M_*^{\rm α_{JAM}}/M_* - σ_e$ correlation is indeed due to the IMF, and stellar M/L gradients can be ignored, then our $ϕ(M_*^{\rm α_{JAM}})$ is an estimate of the stellar mass function in which $σ_e$-dependent variations in the IMF across the population have been accounted for. Using a Fundamental Plane based observational proxy for $σ_e$ produces comparable results. By demonstrating that cheaper proxies are sufficiently accurate, our analysis should enable a more reliable census of the mass in stars for large galaxy samples, at a fraction of the cost. Our results are provided in tabular form.

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arXiv2017

The Morphological Transformation of Red Sequence Galaxies in Clusters since $z \sim 1$

The study of galaxy morphology is fundamental to understand the physical processes driving the structural evolution of galaxies. It has long been known that dense environments host high fractions of early-type galaxies and low fractions of late-type galaxies, indicating that the environment affects the structural evolution of galaxies. In this paper we present an analysis of the morphological composition of red sequence galaxies in a sample of 9 galaxy clusters at $0.8<z<1.5$ drawn from the HAWK-I cluster survey (HCS), with the aim of investigating the evolutionary paths of galaxies with different morphologies. We classify galaxies according to their apparent bulge-to-total light ratio and compare with red sequence galaxies from the lower-redshift WINGS and EDisCS surveys. We find that, while the HCS red sequence is dominated by elliptical galaxies at all luminosities and stellar masses, the WINGS red sequence is dominated by elliptical galaxies only at its bright end ($M_V<-21.0$ mag), while S0s become the most frequent class at fainter luminosities. Disc-dominated galaxies comprise 10-14\% of the red sequence population in the low (WINGS) and high (HCS) redshift samples, although their fraction increases up to 40\% at $0.4 < z < 0.8$ (EDisCS). We find a 20\% increase in the fraction of S0 galaxies from $z \sim 1.5$ to $z \sim 0.05$ on the red sequence. These results suggest that elliptical and S0 galaxies follow different evolutionary histories and, in particular, that S0 galaxies result, at least at intermediate luminosities ($-22.0 < M_V < -20.0$), from the morphological transformation of quiescent spiral galaxies.

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