Related papers: Learning the galaxy-environment connection with gr…
We use the calculations derived in a previous paper (M\'era, Chabrier and Schaeffer, 1997), based on observational constraints arising from star counts, microlensing experiments and kinematic properties, to determine the amount of dark…
We explore the interrelationships between the galaxy group halo mass and various observable group properties. We propose a simple scenario that describes the evolution of the central galaxies and their host dark matter halos. Star formation…
Understanding the scaling relation between baryonic observables and dark matter halo properties is crucial not only for studying galaxy formation and evolution, but also for deriving accurate cosmological constraints from galaxy surveys. In…
Intrinsic alignments of galaxies are measured and modelled to gain cosmological information, to further understand the interactions between galaxies and to mitigate their effects on gravitational weak lensing studies. Hydrodynamical…
McKay et al. (2002) have recently used measurements of weak galaxy-galaxy lensing in the Sloan Digital Sky Survey to estimate the cross correlation between galaxies and the projected dark matter density field. They derive a relation between…
I present some new results related to our understanding of the masses of galaxies both in the local and high-redshift Universe. At high-redshift new Spitzer data on galaxies in the Gemini Deep Deep Survey allow us a more accurate measure of…
Empirical models often rely on key relations from the galaxy--halo connection to construct mock galaxy catalogues. These relations typically describe central galaxies more accurately than satellite galaxies, which are generally less massive…
Galaxies, diffuse gas, and dark matter make up the cosmic web that defines the large-scale structure of the Universe. We constrained the joint distribution of these constituents by cross-correlating galaxy samples binned by stellar mass…
We use high resolution simulations to study the formation and distribution of galaxies within a cluster which forms hierarchically. We follow both dark matter and baryonic gas which is subject to thermal pressure, shocks and radiative…
Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation. Attention-based GNNs infer neighbor importance to manipulate the…
The distribution of matter that is measured through galaxy redshift and peculiar velocity surveys can be harnessed to learn about the physics of dark matter, dark energy, and the nature of gravity. To improve our understanding of the matter…
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…
Galaxy formation is a complex problem that connects large scale cosmology with small scale astrophysics over cosmic timescales. Hydrodynamical simulations are the most principled approach to model galaxy formation, but have large…
We study three cosmological hydrodynamical simulations of Milky Way(MW)-sized halos including a comparison with the dark matter(DM)-only counterparts. We find one of our simulated galaxies with interesting MW-like features. Thanks to a…
We present a novel, deep-learning based method -- dubbed Galactic-Seismology Substructures and Streams Hunter, or GS$^{3}$ Hunter for short, to search for substructures and streams in stellar kinematics data. GS$^{3}$ Hunter relies on a…
We investigate the connection between supermassive black holes (SMBHs), their host galaxies, and large-scale dark-matter halos using broad-line X-ray AGN from the XMM--XXL and Stripe\,82X surveys, together with galaxies from VIPERS and…
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics…
In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and…
We present a method that uses observations of galaxies to simultaneously constrain cosmological parameters and the galaxy-dark matter connection (aka halo occupation statistics). The latter describes how galaxies are distributed over dark…
In this review we outline the major points of ab-initio cosmological models focusing on some galaxy evolution unresolved problems. In particular our knowledge of the star formation rate which leads galaxy evolution, i.e. luminosity, colors…