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The relative roles of mergers and star formation in regulating galaxy growth are still a matter of intense debate. We here present our decode , a new Discrete statistical sEmi-empiriCal mODEl specifically designed to predict rapidly and efficiently, in a full cosmological context, galaxy assembly, and merger histories for any given input stellar mass–halo mass (SMHM) relation. decode generates object-by-object dark matter merger trees (hence discrete) from accurate subhalo mass and infall redshift probability functions (hence statistical) for all subhaloes, including those residing within other subhaloes, with virtually no resolution limits on mass or volume. Merger trees are then converted into galaxy assembly histories via an input, redshift-dependent SMHM relation, which is highly sensitive to the significant systematics in the galaxy stellar mass function and on its evolution with cosmic time. decode can accurately reproduce the predicted mean galaxy merger rates and assembly histories of hydrodynamic simulations and semi-analytical models, when adopting in input their SMHM relations. In this work, we use decode to prove that only SMHM relations implied by stellar mass functions characterized by large abundances of massive galaxies and significant redshift evolution, at least at |$M_\star \gtrsim 10^{11} \, \mathrm{M}_\odot$|⁠ , can simultaneously reproduce the local abundances of satellite galaxies, the galaxy (major merger) pairs since z ∼ 3, and the growth of Brightest Cluster Galaxies. The same models can also reproduce the local fraction of elliptical galaxies, on the assumption that these are strictly formed by major mergers, but not the full bulge-to-disc ratio distributions, which require additional processes. [ABSTRACT FROM AUTHOR]

With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect |${\sim}90{{\ \rm per\ cent}}$| of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models. The code used in this is available at https://github.com/carlamb/astronomical-outliers-WGAN. [ABSTRACT FROM AUTHOR]

The mass and structural assembly of galaxies is a matter of intense debate. Current theoretical models predict the existence of a linear relationship between galaxy size (R e) and the host dark matter halo virial radius (R h). By making use of semi-empirical models compared to the size distributions of central galaxies from the Sloan Digital Sky Survey, we provide robust constraints on the normalization and scatter of the R e− R h relation. We explore the parameter space of models in which the R e− R h relation is mediated by either the spin parameter or the concentration of the host halo, or a simple constant the nature of which is in principle unknown. We find that the data require extremely tight relations for both early-type and late-type galaxies (ETGs, LTGs), especially for more massive galaxies. These constraints challenge models based solely on angular momentum conservation, which predict significantly wider distributions of galaxy sizes and no trend with stellar mass, if taken at face value. We discuss physically motivated alterations to the original models that bring the predictions into better agreement with the data. We argue that the measured tight size distributions of SDSS disc galaxies can be reproduced by semi-empirical models in which the R e− R h connection is mediated by the stellar specific angular momenta j star. We find that current cosmological models of galaxy formation broadly agree with our constraints for LTGs, and justify the strong link between R e and j star that we propose, however the tightness of the R e− R h relation found in such ab initio theoretical models for ETGs is in tension with our semi-empirical findings. [ABSTRACT FROM AUTHOR]

We carry out a systematic investigation of the total mass density profile of massive (logMstar/M☉ ~ 11.5) early-type galaxies and its dependence on redshift, specifically in the range 0 ≳ z ≳ 1. We start from a large sample of Sloan Digital Sky Survey early-type galaxies with stellar masses and effective radii measured assuming two different profiles, de Vaucouleurs and Sérsic. We assign dark matter haloes to galaxies via abundance matching relations with standard ΛCDM profiles and concentrations. We then compute the total, massweighted density slope at the effective radius γ γ, and study its redshift dependence at fixed stellar mass. We find that a necessary condition to induce an increasingly flatter γ γ at higher redshifts, as suggested by current strong lensing data, is to allow the intrinsic stellar profile of massive galaxies to be Sérsic and the input Sérsic index n to vary with redshift as n(z) - (1 + z)δ, with δ ≳ -1. This conclusion holds irrespective of the input Mstar-Mhalo relation, the assumed stellar initialmass function (IMF), or even the chosen level of adiabatic contraction in the model. Secondary contributors to the observed redshift evolution of γ γ may come from an increased contribution at higher redshifts of adiabatic contraction and/or bottom-light stellar IMFs. The strong lensing selection effects we have simulated seem not to contribute tothis effect. A steadily increasing Sérsic index with cosmic time is supported by independent observations, though it is not yet clear whether cosmological hierarchical models (e.g. mergers) are capable of reproducing such a fast and sharp evolution. [ABSTRACT FROM AUTHOR]