Using dust to constrain dark matter models

TL;DR

This study uses hydrodynamic simulations to show that dust distribution differences can help distinguish between cold and warm dark matter models in Milky Way-like galaxies.

Contribution

It introduces a novel method of using dust as an observational tracer to differentiate dark matter models, based on simulation comparisons of dust distributions.

Findings

Cold dark matter results in ~4.5 times more dust than warm dark matter.

Dust distribution in CDM is more concentrated than in WDM.

Dust can serve as a unique observational probe for dark matter properties.

Abstract

In this paper, we use hydrodynamic zoom-in simulations of Milky Way-type haloes to explore using dust as an observational tracer to discriminate between cold and warm dark matter (WDM) universes. Comparing a cold and 3.5 keV WDM particle model, we tune the efficiency of galaxy formation in our simulations using a variable supernova rate to create Milky Way systems with similar satellite galaxy populations while keeping all other simulation parameters the same. Cold dark matter (CDM), having more substructure, requires a higher supernova efficiency than WDM to achieve the same satellite galaxy number. These different supernova efficiencies create different dust distributions around their host galaxies, which we generate by post-processing the simulation output with the \powderday{} codebase. Analysing the resulting dust in each simulation, we find $\sim$4.5 times more dust in our CDM Milky Way haloes compared with WDM. The distribution of dust out to $R_{200\text{c}}$ is then explored, revealing that the WDM simulations are noticeably less concentrated than their CDM counterparts, although differences in substructure complicate the comparison. Our results indicate that dust is a possible unique probe to test theories of dark matter.