Weighing gas-rich starless halos: dark matter parameters inference from their gas distributions

TL;DR

This paper develops a Bayesian framework to accurately infer dark matter halo masses from gas distributions in starless RELHICs, addressing biases and degeneracies caused by environmental effects.

Contribution

It introduces a method to recover halo parameters from gas profiles, accounting for environmental influences and demonstrating reliable mass estimation.

Findings

Ensemble inference accurately recovers halo virial mass from 3D profiles.

Environmental density causes a mass-concentration degeneracy in individual systems.

Accounting for environmental density removes systematic mass biases.

Abstract

Reionization-Limited $H_{I}$ Clouds (RELHICs) are starless dark matter halos retaining a significant neutral hydrogen($H_{I}$ ) reservoir. The gas resides in near hydrostatic equilibrium within the dark matter potential and in thermal equilibrium with the cosmic ultraviolet background. This simplicity allows analytic frameworks to link observable $H_{I}$ column densities directly to fundamental dark matter halo structural parameters. We systematically assess the accuracy of inferring host halo parameters from RELHIC gas distributions on an object-by-object basis, quantifying biases, intrinsic degeneracies, and the limits of parameter recovery. Using RELHICs from a redshift z = 0 high-resolution cosmological hydrodynamical simulation, we employ Bayesian nested sampling to infer dark matter halo mass and concentration. We evaluate this against 3D spherically averaged total gas and $H_{I}$ density profiles, alongside 2D $H_{I}$ column density profiles. We found that while the ensemble inference yields a robust, unbiased recovery of halo virial mass from 3D profiles, individual systems exhibit a mass-concentration degeneracy driven by local environmental density. Overdense environments yield slightly overestimated masses and underestimated concentrations; underdense regions show the inverse. We demonstrate that treating environmental density as a free parameter breaks this degeneracy and completely neutralizes the systematic mass bias. Although concentration recovery remains limited by simulation resolution, the virial mass is exceptionally well constrained, establishing a highly reliable framework for weighing starless halos in upcoming surveys.