Related papers: A Machine Learning Approach to Correct for Mass Re…
The structural and dynamic properties of the dark matter halos, though an important ingredient in understanding large-scale structure formation, require more conservative particle resolution than those required by halo mass alone in a…
Despite the Milky Way's proximity to us, our knowledge of its dark matter halo is fairly limited, and there is still considerable uncertainty in its halo mass. Many past techniques have been limited by assumptions such as the Galaxy being…
We introduce the Lomonosov suite of high-resolution N-body cosmological simulations covering a full box of size 32 $h^{-1}$ Mpc with low-mass resolution particles ($2\times10^7$ $h^{-1}\,M_\odot$) and three zoom-in simulations of overdense,…
We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation. Different methods are tested against a mock cluster catalog constructed…
We provide a new observational test for a key prediction of the \Lambda CDM cosmological model: the contributions of mergers with different halo-to-main-cluster mass ratios to cluster-sized halo growth. We perform this test by dynamically…
Accurate predictions of the abundance and clustering of dark matter haloes play a key role in testing the standard cosmological model. Here, we investigate the accuracy of one of the leading methods of connecting the simulated dark matter…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
Massive halos hosting groups and clusters of galaxies imprint coherent, arcminute-scale features across the spectrophotometric sky, especially optical-IR clusters of galaxies, distortions in the sub-mm CMB, and extended sources of X-ray…
The simplest scheme for predicting real galaxy properties after performing a dark matter simulation is to rank order the real systems by stellar mass and the simulated systems by halo mass and then simply assume monotonicity - that the more…
We apply a novel method with machine learning to calibrate sub-grid models within numerical simulation codes to achieve convergence with observations and between different codes. It utilizes active learning and neural density estimators.…
High-resolution cosmological N-body simulations are excellent tools for modelling the formation and clustering of dark matter haloes. These simulations suggest complex physical theories of halo formation governed by a set of effective…
When constructing galaxy mock catalogs based on suits of dark matter halo catalogs generated with approximated, calibrated or machine-learning approaches, the assignment of intrinsic properties for such tracers is a step of paramount…
We present a test to quantify how well some approximate methods, designed to reproduce the mildly non-linear evolution of perturbations, are able to reproduce the clustering of DM halos once the grouping of particles into halos is defined…
We present an accurate and fast framework for generating mock catalogues including low-mass halos, based on an implementation of the COmoving Lagrangian Acceleration (COLA) technique. Multiple realisations of mock catalogues are crucial for…
We present ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$, a new machine learning approach for inferring the mass of host dark matter halos, $M_h$, from the photometry and morphology of galaxies. ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$…
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…
The clustering properties of dark matter halos are a firm prediction of modern theories of structure formation. We use two large volume, high-resolution N-body simulations to study how the correlation function of massive dark matter halos…
We present analysis of the evolution of dark matter halos in dense environments of groups and clusters in dissipationless cosmological simulations. The premature destruction of halos in such environments, known as the overmerging, reduces…
We investigate the ability of machine learning to infer the virial mass ($M_{\rm vir}$) and the scale radius ($r_{\rm s}$) of galaxy clusters from their observables. Using the Uchuu--UniverseMachine galaxy catalog at $z=0.093$, we generate…
We develop and test an algorithm to rescale a simulated dark-matter particle distribution or halo catalogue from a standard gravity model to that of a modified gravity model. This method is based on that of Angulo & White but with some…