Hints on the Nature of Dark Matter from the Properties of Milky Way Satellites

TL;DR

This study uses high-resolution simulations of Milky Way satellites to constrain dark matter properties, demonstrating that multiple observational constraints can effectively rule out certain dark matter models on small scales.

Contribution

The paper introduces a multi-constraint approach using satellite galaxy observations to limit dark matter models, including mixed cold and warm dark matter scenarios.

Findings

Standard CDM models face challenges on small scales.

Warm dark matter with 2 keV mass is constrained by satellite data.

Mixed cold plus warm dark matter models are tightly limited by combined observations.

Abstract

The nature of dark matter is still unknown and one of the most fundamental scientific mysteries. Although successfully describing large scales, the standard cold dark matter model (CDM) exhibits possible shortcomings on galactic and sub-galactic scales. It is exactly at these highly non-linear scales where strong astrophysical constraints can be set on the nature of the dark matter particle. While observations of the Lyman-$\alpha$ forest probe the matter power spectrum in the mildly non-linear regime, satellite galaxies of the Milky Way provide an excellent laboratory as a test of the underlying cosmology on much smaller scales. Here we present results from a set of high resolution simulations of a Milky Way sized dark matter halo in eight distinct cosmologies: CDM, warm dark matter (WDM) with a particle mass of 2 keV and six different cold plus warm dark matter (C+WDM) models, varying the fraction, $f_{\rm wdm}$, and the mass, $m_{\rm wdm}$, of the warm component. We used three different observational tests based on Milky Way satellite observations: the total satellite abundance, their radial distribution and their mass profile. We show that the requirement of simultaneously satisfying all three constraints sets very strong limits on the nature of dark matter. This shows the power of a multi-dimensional small scale approach in ruling out models which would be still allowed by large scale observations.