Using dust to constrain dark matter models

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 universes. Comparing a cold and 3.5keV warm dark matter 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, having more substructure, requires a higher supernova efficiency than warm dark matter 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 ∼4.5 times more dust in our cold dark matter Milky Way halos compared with warm dark matter. The distribution of dust out to R200c is then explored, revealing that the warm dark matter simulations are noticeably less concentrated than their cold dark matter counterparts, although differences in substructure complicate the comparison. Our results indicate that dust is a possible unique probe to test theories of dark matter.