Dark matter halo properties from spatially integrated HI flux profiles

TL;DR

This paper develops a Bayesian model to infer dark matter halo properties from the full shape of spatially integrated HI flux profiles, providing a resource-efficient alternative to rotation curves with comparable accuracy.

Contribution

It introduces a novel Bayesian approach that utilizes the entire HI flux profile shape, improving constraints on halo parameters over traditional linewidth methods.

Findings

Flux profile inference yields three times tighter halo parameter posteriors than linewidth.

The model shows good agreement with rotation curve results for symmetric profiles.

Empirical HI distribution model enables application to unresolved datasets.

Abstract

Resolved rotation curves (RCs) are our best probe of the dark matter distribution around individual galaxies. However their acquisition is resource-intensive, rendering them impractical for large-scale surveys and studies at higher redshift. Spatially integrated HI flux profiles on the other hand are observationally abundant and also probe dynamics across the whole HI disc. Despite this, they are typically only studied using the highly compressed linewidth summary statistic, discarding much of the available information. Here we construct a Bayesian model to infer halo properties from the full shape of the spatially integrated 21-cm line profile of a galaxy, utilising all the available information. We validate our model by assessing the consistency of halo parameters obtained from the flux profile with those obtained from RC fits for galaxies where both are available, finding good agreement provided the profile is not strongly asymmetric. We study the relative constraining power (quantified using the Kullback--Leibler divergence of the posterior from the prior), finding the flux profile inference recovers posteriors on generalised Navarro--Frenk--White halo parameters on average three times tighter than those from the linewidth, and in some cases as tight as those from resolved RCs. Finally we introduce and validate a probabilistic empirical model for the spatial distribution of HI, enabling our model to be applied to datasets for which no spatially resolved HI information is available. As the next-generation of HI observatories comes online, our framework will enable mass modelling in new regimes, with particular utility for constraining the dark matter content of galaxies across cosmic time.