相关论文: Painting galaxies into dark matter halos using mac…

We use an empirical approach to model the stellar mass of galaxies according to their host dark-matter haloes and subhaloes ('HASH'), where each galaxy resides in a subhalo taken from a large N-body cosmological simulation. This approach…

We present a new prescription for the halo mass function (HMF) built upon the Evolution Mapping framework. This approach provides a physical motivation to parametrise the non-universality of the HMF in terms of the recent history of…

Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a…

Precise halo mass ($M_h$) measurements are crucial for cosmology and galaxy formation. HaloFlow introduced a simulation-based inference (SBI) framework that uses state-of-the-art simulated galaxy images to precisely infer $M_h$. However,…

Dark matter-only simulations predict that dark matter halos have steep, cuspy inner density profiles, while observations of dwarf galaxies find a range of inner slopes that are often much shallower. There is debate whether this discrepancy…

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.…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

We investigate how reliably the global properties of Milky Way-mass dark matter haloes can be recovered from dynamical data over a limited radial range, particularly $\lesssim 30~\mathrm{kpc}$ where observations are most sensitive but…

We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…

The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex…

We investigate the internal structure of elliptical galaxies at $z\sim 0.2$ from a joint lensing-dynamics analysis. We model Hubble Space Telescope images of a sample of 23 galaxy-galaxy lenses selected from the Sloan Lens ACS (SLACS)…

Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Forest using mock catalogs of clusters from Magneticum…

We provide a quantitative analysis of the halo model in the context of massive neutrino cosmologies. We discuss all the ingredients necessary to model the non-linear matter and cold dark matter power spectra and compare with the results of…

We present an analysis of the effects of baryon physics on the halo mass function. The analysis is based on simulations of a cosmological volume. Besides a Dark Matter (DM) only simulation, we also carry out two other hydrodynamical…

Sparse regression algorithms have been proposed as the appropriate framework to model the governing equations of a system from data, without needing prior knowledge of the underlying physics. In this work, we use sparse regression to build…

The MACHO collaboration reports on the analysis of our first year LMC data, 9.5 million light curves with an average of 235 observations each. Automated selection procedures give 3 events consistent with microlensing. We evaluate our…

Properties of galaxies vary systematically with the mass of their parent dark matter halos. This basic galaxy - halo connection shows a fair amount of scatter whose origin is not fully understood. Here, we study how differences in the halo…

We present a method for populating dark matter simulations with haloes of mass below the resolution limit. It is based on stochastically sampling a field derived from the density field of the halo catalogue, using constraints from the…

We present a deep-learning-based approach for identifying dark matter haloes in cosmological N-body simulations. Our framework consists of a volumetric Convolutional Neural Network to classify individual simulation particles as either halo…

We explore a function with two shape parameters for the dark-matter halo density profile subject to baryonic effects, which is a special case of the general Zhao family of models applied to simulated dark matter haloes by Dekel et al. This…