Related papers: How the Galaxy-Halo Connection Depends on Large-Sc…
The recently discovered discrepancy between galaxy mass measurements from weak lensing and predictions from abundance matching questions our understanding of cosmology, or of the galaxy-halo connection, or of both. We re-examined this…
We perform an analysis of the Cosmic Web as a complex network, which is built on a $\Lambda$CDM cosmological simulation. For each of nodes, which are in this case dark matter halos formed in the simulation, we compute 10 network metrics,…
We perform a consistent comparison of the mass and mass profiles of massive ($M_\star > 10^{11.4}M_{\odot}$) central galaxies at z~0.4 from deep Hyper Suprime-Cam (HSC) observations and from the Illustris, TNG100, and Ponos simulations.…
We use ~100 square deg of deep (>28.5 mag arcsec$^{-2}$ in i-band), high-quality (median 0.6 arcsec seeing) imaging data from the Hyper Suprime-Cam (HSC) survey to reveal the halo mass dependence of the surface mass density profiles and…
Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter halos. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models…
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only…
We measure the clustering of dark matter halos in a large set of collisionless cosmological simulations of the flat LCDM cosmology. Halos are identified using the spherical overdensity algorithm, which finds the mass around isolated peaks…
We examine the projected correlation of galaxies with mass from small scales (<few hundred kpc) where individual dark matter halos dominate, out to 15 Mpc where correlated large-scale structure dominates. We investigate these profiles as a…
A generic prediction of hierarchical clustering models is that the mass function of dark haloes in dense regions in the Universe should be top-heavy. We provide a novel test of this prediction using a sample of galaxies drawn from the Sloan…
Galaxy mergers play an important role in many aspects of galaxy evolution, therefore, more accurate merger identifications are paramount for achieving a complete understanding of galaxy evolution. As we enter the era of very large imaging…
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…
The scaling relations between supermassive black holes and their host galaxy properties are of fundamental importance in the context black hole-host galaxy co-evolution throughout cosmic time. In this work, we use a novel algorithm that…
Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs,…
Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to…
Dark matter haloes and subhaloes that host no luminous counterpart are predicted within our current understanding of galaxy formation within a $\Lambda$CDM paradigm. Observational tests, such as gravitational lensing, have made potential…
Using a novel machine learning method, we investigate the buildup of galaxy properties in different simulations, and in various environments within a single simulation. The aim of this work is to show the power of this approach at…
We explore two widely used empirical models for the galaxy-halo connection, subhalo abundance matching (SHAM) and the halo occupation distribution (HOD) and compare their predictions with the hydrodynamical simulation IllustrisTNG (TNG) for…
We present a novel graph-based machine learning classifier for identifying the dark matter cosmic web environments of galaxies. Large galaxy surveys offer comprehensive statistical views of how galaxy properties are shaped by large-scale…
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for…
We develop a framework to study the relation between the stellar mass of a galaxy and the total mass of its host dark matter halo using galaxy clustering and galaxy-galaxy lensing measurements. We model a wide range of scales, roughly from…