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We propose a parametric model for studying self-interacting dark matter (SIDM) halos. The model uses an analytical density profile, calibrated using a controlled N-body SIDM simulation that covers the entire gravothermal evolution,…
We present a simple model to describe the dark matter density, the gas density, and the gas temperature profiles of galaxy clusters. Analytical expressions for these quantities are given in terms of only five free parameters with a clear…
We explore the possibility that the density profiles of elliptical galaxies and cold dark matter (CDM) halos found in cosmological simulations can be understood in terms of the same physical process, collisionless gravitational collapse. To…
We use techniques from nonparametric function estimation theory to extract the density profiles, and their derivatives, from a set of N-body dark matter halos. We consider halos generated from LCDM simulations of gravitational clustering,…
We use numerical simulations of critically-closed cold dark matter (CDM) models to study the effects of numerical resolution on observable quantities. We study simulations with up to $256^3$ particles using the particle-mesh (PM) method and…
Recent claims of observational evidence for self-interacting dark matter (SIDM) have relied on a semi-analytic method for predicting the density profiles of galaxies and galaxy clusters containing SIDM. We present a thorough description of…
We use numerical simulations in a Lambda CDM cosmology to model density profiles in a set of 16 dark matter haloes with resolutions of up to 7 million particles within the virial radius. These simulations allow us to follow robustly the…
Using an analytic model calibrated against numerical simulations, we calculate the central densities of dark matter halos in a ``conventional'' cold dark matter model with a cosmological constant (LCDM) and in a ``tilted'' model (TLCDM)…
Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework for disentangling representations of thermal…
Detecting diffuse radio emission, such as from halos, in galaxy clusters is crucial for understanding large-scale structure formation in the universe. Traditional methods, which rely on X-ray and Sunyaev-Zeldovich (SZ) cluster…
Cosmological simulations of galaxy formation predict a universal form for the mass profile of dark matter (DM) halos from cluster to galaxy scales. Remarkably few interesting constraints exist, however, on DM halos in early-type galaxies.…
We present a simple regulator-type framework designed specifically for modelling formation of dwarf galaxies. Despite its simplicity, when coupled with realistic mass accretion histories of haloes from simulations and reasonable choices for…
Recent advances in observational astronomy allow to study various groups of Dark Matter (DM) dominated objects from the dwarf spheroidal (dSph) galaxies to clusters of galaxies that span the mass range from $10^{6} M_{\odot}$ to $10^{15}…
We present a masked-guided approach for a denoising diffusion probabilistic model (DDPM) trained to generate and inpaint realistic radio galaxy images. The inpainting capability is particularly relevant for reconstructing incomplete…
In this paper, we formulate a new model of density distribution for halos made of warm dark matter (WDM) particles. The model is described by a single microphysics parameter - the mass (or, equivalently, the maximal value of the initial…
Recent observations and simulations reveal that the circumgalactic medium (CGM) surrounding galaxies is multiphase, with the gas temperatures spanning a wide range at most radii, $\sim 10^4\ {\rm K}$ to the virial temperature ($\sim 10^6$ K…
Denoising Diffusion Probabilistic Models (DDPM) process images as a whole. Since adjacent pixels are highly likely to belong to the same object, we propose the Heat Diffusion Model (HDM) to further preserve image details and generate more…
We show that a Denoising Diffusion Probabalistic Model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic…
We present new constraints on the density profiles of dark matter (DM) halos in seven nearby dwarf galaxies from measurements of their integrated stellar light and gas kinematics. The gas kinematics of low mass galaxies frequently suggest…
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis,…