Related papers: $\texttt{Mangrove}$: Learning Galaxy Properties fr…
We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic…
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation. To this end, we demonstrate a CNN-based regression model that is able to…
In the context of the hierarchical formation of galaxies, we investigated the role played by mergers in shaping the scale relations of galaxies, that is the projections of their Fundamental Plane onto the \IeRe, \IeSig, \MRa\ and \Lsig\…
Linking the properties of galaxies to the assembly history of their dark matter haloes is a central aim of galaxy evolution theory. This paper introduces a dimensionless parameter $s\in[0,1]$, the "tree entropy", to parametrise the geometry…
The estimation of the bulge and disk massses, the main baryonic components of a galaxy, can be performed using various approaches, but their implementation tend to be challenging as they often rely on strong assumptions about either the…
In hierarchical structure formation, merging of galaxies is frequent and known to dramatically affect their properties. To comprehend these interactions high-resolution simulations are indispensable because of the nonlinear coupling between…
Semi-analytic models (SAMs) are a promising means of tracking the physical processes associated with galaxy formation, but many of their approximations have not been rigorously tested. As part of the SMAUG (Simulating Multiscale…
We have constructed the merging history of dark matter halos in a SCDM cosmology, by means of a the {``Merging Cell Model''} (Rodrigues & Thomas 1996). It is based on the linear theory of growth of density fluctuations, and the Top-Hat…
Upcoming cosmological surveys will provide unprecedented amount of data, which will require innovative statistical methods to maximize the scientific exploitation. Standard cosmological analyses based on abundances, two-point and…
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive flows to stars from a realistic simulation of a Milky…
We have undertaken numerical simulations of galaxy interactions and mergers, coupling the dynamics with the star formation history and the chemical evolution. The self-gravity of stars and gas is taken into account through a tree-code…
Hydrodynamic simulations provide a powerful, but computationally expensive, approach to study the interplay of dark matter and baryons in cosmological structure formation. Here we introduce the EMulating Baryonic EnRichment (EMBER) Deep…
The evolution of the large-scale distribution of matter is sensitive to a variety of fundamental parameters that characterise the dark matter, dark energy, and other aspects of our cosmological framework. Since the majority of the mass…
We analyze the total and baryonic acceleration profiles of a set of well-resolved galaxies identified in the EAGLE suite of hydrodynamic simulations. Our runs start from the same initial conditions but adopt different prescriptions for…
We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass ($M_\ast$), star-formation rate (SFR), atomic and molecular gas masses…
We use hydrodynamic simulations to examine how the baryonic components of galaxies are assembled, focusing on the relative importance of mergers and smooth accretion in the formation of ~L_* systems. In our primary simulation, which models…
Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of…
The formation of structure in the Universe at large scales is dominated by gravity, with baryonic physics becoming significant at $\sim{\rm Mpc}$ scales. To capture the impact of baryonic physics, cosmological simulations must model gas…
Cosmological simulations like CAMELS and IllustrisTNG characterize hundreds of thousands of galaxies using various internal properties. Previous studies have demonstrated that machine learning can be used to infer the cosmological parameter…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…