Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available bayesian automated classification

We present an automated morphological classification in 4 types (E,S0,Sab,Scd) of ~700.000 galaxies from the SDSS DR7 spectroscopic sample based on support vector machines. The main new property of the classification is that we associate to each galaxy a probability of being in the four morphological classes instead of assigning a single class. The classification is therefore better adapted to nature where we expect a continuos transition between different morphological types. The algorithm is trained with a visual classification and then compared to several independent visual classifications including the Galaxy Zoo first release catalog. We find a very good correlation between the automated classification and classical visual ones. The compiled catalog is intended for use in different applications and can be downloaded at http://gepicom04.obspm.fr/sdss_morphology/Morphology_2010.html and soon from the CasJobs database.Comment: A&A in press, english corrections from language editor adde

Soft clustering analysis of galaxy morphologies: A worked example with SDSS

Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover classes automatically. Aims: We briefly discuss the pitfalls of oversimplified classification methods and outline an alternative approach called "clustering analysis". Methods: We categorise different classification methods according to their capabilities. Based on this categorisation, we present a probabilistic classification algorithm that automatically detects the optimal classes preferred by the data. We explore the reliability of this algorithm in systematic tests. Using a small sample of bright galaxies from the SDSS, we demonstrate the performance of this algorithm in practice. We are able to disentangle the problems of classification and parametrisation of galaxy morphologies in this case. Results: We give physical arguments that a probabilistic classification scheme is necessary. The algorithm we present produces reasonable morphological classes and object-to-class assignments without any prior assumptions. Conclusions: There are sophisticated automated classification algorithms that meet all necessary requirements, but a lot of work is still needed on the interpretation of the results.Comment: 18 pages, 19 figures, 2 tables, submitted to A

A brief review of contrastive learning applied to astrophysics

Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical datasets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multi-dimensional datasets, which has become increasingly popular in the computer vision and Machine Learning communities in recent years. To do so, it maximizes the agreement between the information extracted from augmented versions of the same input data, making the final representation invariant to the applied transformations. Contrastive Learning is particularly useful in astronomy for removing known instrumental effects and for performing supervised classifications and regressions with a limited amount of available labels, showing a promising avenue towards \emph{Foundation Models}. This short review paper briefly summarizes the main concepts behind contrastive learning and reviews the first promising applications to astronomy. We include some practical recommendations on which applications are particularly attractive for contrastive learning.Comment: Invited review to be published in RAST

The bivariate gas-stellar mass distributions and the mass functions of early- and late-type galaxies at z∼0z\sim0

We report the bivariate HI- and H$_2$-stellar mass distributions of local galaxies in addition of an inventory of galaxy mass functions, MFs, for HI, H$_2$, cold gas, and baryonic mass, separately into early- and late-type galaxies. The MFs are determined using the HI and H$_2$ conditional distributions and the galaxy stellar mass function, GSMF. For the conditional distributions we use the compilation presented in Calette et al. 2018. For determining the GSMF from $M_{\ast}\sim3\times10^{7}$ to $3\times10^{12}$ $M_{\odot}$, we combine two spectroscopic samples from the SDSS at the redshift range $0.0033<z<0.2$. We find that the low-mass end slope of the GSMF, after correcting from surface brightness incompleteness, is $\alpha\approx-1.4$, consistent with previous determinations. The obtained HI MFs agree with radio blind surveys. Similarly, the H$_2$ MFs are consistent with CO follow-up optically-selected samples. We estimate the impact of systematics due to mass-to-light ratios and find that our MFs are robust against systematic errors. We deconvolve our MFs from random errors to obtain the intrinsic MFs. Using the MFs, we calculate cosmic density parameters of all the baryonic components. Baryons locked inside galaxies represent 5.4% of the universal baryon content, while $\sim96$% of the HI and H$_2$ mass inside galaxies reside in late-type morphologies. Our results imply cosmic depletion times of H$_2$ and total neutral H in late-type galaxies of $\sim 1.3$ and 7.2 Gyr, respectively, which shows that late type galaxies are on average inefficient in converting H$_2$ into stars and in transforming HI gas into H$_2$. Our results provide a fully self-consistent empirical description of galaxy demographics in terms of the bivariate gas--stellar mass distribution and their projections, the MFs. This description is ideal to compare and/or to constrain galaxy formation models.Comment: 37 pages, 17 figures. Accepted for publication in PASA. A code that displays tables and figures with all the relevant statistical distributions and correlations discussed in this paper is available here https://github.com/arcalette/Python-code-to-generate-Rodriguez-Puebla-2020-result

Galaxy size trends as a consequence of cosmology

We show that recently documented trends in galaxy sizes with mass and redshift can be understood in terms of the influence of underlying cosmic evolution; a holistic view which is complimentary to interpretations involving the accumulation of discreet evolutionary processes acting on individual objects. Using standard cosmology theory, supported with results from the Millennium simulations, we derive expected size trends for collapsed cosmic structures, emphasising the important distinction between these trends and the assembly paths of individual regions. We then argue that the observed variation in the stellar mass content of these structures can be understood to first order in terms of natural limitations of cooling and feedback. But whilst these relative masses vary by orders of magnitude, galaxy and host radii have been found to correlate linearly. We explain how these two aspects will lead to galaxy sizes that closely follow observed trends and their evolution, comparing directly with the COSMOS and SDSS surveys. Thus we conclude that the observed minimum radius for galaxies, the evolving trend in size as a function of mass for intermediate systems, and the observed increase in the sizes of massive galaxies, may all be considered an emergent consequence of the cosmic expansion.Comment: 14 pages, 13 figures. Accepted by MNRA

The role of environment in the morphological transformation of galaxies in 9 intermediate redshift clusters

[abridged] We analyze a sample of 9 massive clusters at 0.4<z<0.6 observed with MegaCam in 4 photometric bands (g,r,i,z) from the core to a radius of 5 Mpc (~4000 galaxies). Galaxy cluster candidates are selected using photometric redshifts computed with HyperZ. Morphologies are estimated with galSVM in two broad morphological types (early-type and late-type). We examine the morphological composition of the red-sequence and the blue-cloud and study the relations between galaxies and their environment through the morphology-density relations (T-Sigma) and the morphology-radius relation (T-R) in a mass limited sample (log(M/Msol)>9.5). We find that the red sequence is already in place at z~0.5 and it is mainly composed of very massive (log(M/Msol)>11.3) early-type galaxies. These massive galaxies seem to be already formed when they enter the cluster, probably in infalling groups, since the fraction remains constant with the cluster radius. Their presence in the cluster center could be explained by a segregation effect reflecting an early assembly history. Any evolution that takes place in the galaxy cluster population occurs therefore at lower masses (10.3<log(M/Msol)<11.3). For these galaxies, the evolution, is mainly driven by galaxy-galaxy interactions in the outskirts as revealed by the T-Sigma relation. Finally, the majority of less massive galaxies (9.5<log(M/Msol)<10.3) are late-type galaxies at all locations, suggesting that they have not started the morphological transformation yet even if this low mass bin might be affected by incompleteness.Comment: A&A in pres

A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description

We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS classification but with an unlimited number of dimensions and non-linear boundaries between decision regions, is fully automated and thus particularly well adapted to large cosmological surveys. The source code is available for download at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method, we use a seeing limited near-infrared ($K_s$ band, $2,16\mu m$) sample observed with WIRCam at CFHT at a median redshift of $z\sim0.8$. The machine is trained with a simulated sample built from a local visually classified sample from the SDSS chosen in the high-redshift sample's rest-frame (i band, $0.77\mu m$) and artificially redshifted to match the observing conditions. We use a 12-dimensional volume, including 5 morphological parameters and other caracteristics of galaxies such as luminosity and redshift. We show that a qualitative separation in two main morphological types (late type and early type) can be obtained with an error lower than 20% up to the completeness limit of the sample ($KAB\sim 22$) which is more than 2 times better that what would be obtained with a classical C/A classification on the same sample and indeed comparable to space data. The method is optimized to solve a specific problem, offering an objective and automated estimate of errors that enables a straightforward comparison with other surveys.Comment: 11 pages, 7 figures, 3 tables. Submitted to A&A. High resolution images are available on reques

Comparing PyMorph and SDSS photometry. II. The differences are more than semantics and are not dominated by intracluster light

The Sloan Digital Sky Survey pipeline photometry underestimates the brightnesses of the most luminous galaxies. This is mainly because (i) the SDSS overestimates the sky background and (ii) single or two-component Sersic-based models better fit the surface brightness profile of galaxies, especially at high luminosities, than does the de Vaucouleurs model used by the SDSS pipeline. We use the PyMorph photometric reductions to isolate effect (ii) and show that it is the same in the full sample as in small group environments, and for satellites in the most massive clusters as well. None of these are expected to be significantly affected by intracluster light (ICL). We only see an additional effect for centrals in the most massive halos, but we argue that even this is not dominated by ICL. Hence, for the vast majority of galaxies, the differences between PyMorph and SDSS pipeline photometry cannot be ascribed to the semantics of whether or not one includes the ICL when describing the stellar mass of massive galaxies. Rather, they likely reflect differences in star formation or assembly histories. Failure to account for the SDSS underestimate has significantly biased most previous estimates of the SDSS luminosity and stellar mass functions, and therefore Halo Model estimates of the z ~ 0.1 relation between the mass of a halo and that of the galaxy at its center. We also show that when one studies correlations, at fixed group mass, with a quantity which was not used to define the groups, then selection effects appear. We show why such effects arise, and should not be mistaken for physical effects.Comment: 15 pages, 17 figures, accepted for publication in MNRAS. The PyMorph luminosities and stellar masses are available at https://www.physics.upenn.edu/~ameert/SDSS_PhotDec

A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. II. Quantifying morphological k-correction in the COSMOS field at 1<z<2: Ks band vs. I band

We quantify the effects of \emph{morphological k-correction} at $1<z<2$ by comparing morphologies measured in the K and I-bands in the COSMOS area. Ks-band data have indeed the advantage of probing old stellar populations for $z<2$, enabling a determination of galaxy morphological types unaffected by recent star formation. In paper I we presented a new non-parametric method to quantify morphologies of galaxies on seeing limited images based on support vector machines. Here we use this method to classify $\sim$$50 000$ $Ks$ selected galaxies in the COSMOS area observed with WIRCam at CFHT. The obtained classification is used to investigate the redshift distributions and number counts per morphological type up to $z\sim2$ and to compare to the results obtained with HST/ACS in the I-band on the same objects from other works. We associate to every galaxy with $Ks<21.5$ and $z<2$ a probability between 0 and 1 of being late-type or early-type. The classification is found to be reliable up to $z\sim2$. The mean probability is $p\sim0.8$. It decreases with redshift and with size, especially for the early-type population but remains above $p\sim0.7$. The classification is globally in good agreement with the one obtained using HST/ACS for $z<1$. Above $z\sim1$, the I-band classification tends to find less early-type galaxies than the Ks-band one by a factor $\sim$1.5 which might be a consequence of morphological k-correction effects. We argue therefore that studies based on I-band HST/ACS classifications at $z>1$ could be underestimating the elliptical population. [abridged]Comment: accepted for publication in A&A, updated with referee comments, 12 pages, 10 figure