A Universal Distribution of Dark Matter in Milky Way-like galaxies and How to Infer It

Abstract

The phase-space density of dark matter within the Milky Way is a key quantity that encodes information about the nature of the dark sector. The local phase-space density is also required to properly interpret the results of dark matter direct detection experiments. However, there are at present few observational constraints. In this paper, we show that a simple coordinate transformation reveals a near-universal DM phase-space distribution function among three independent suites of cosmological simulations of Milky Way-mass galaxies. We provide evidence for this with plots of kinematic features as well as machine learning-based classifiers that are sensitive to all of the correlations in the full multivariate phase space. Deviations from universality are found only at extremes of galactic radius and/or velocity, and in one simulation that has a prominent accreted dark disc. We further show that the parameters for the coordinate transformation can be inferred from metal poor stars ($\log_{10}[{\rm Fe}/{\rm H}]< -2$). These stars also contain signatures of the dark disk, allowing the existence of such a structure to be inferred from observation. Finally, we construct a model of this universal phase-space distribution using a normalizing flow, trained on the standardized phase-space across simulations. We will apply our method to survey data from Gaia and SDSS in a forthcoming work.