Dark Matter in 3D

TL;DR

This paper introduces a new method to analyze directional dark matter detection data by parameterizing the local dark matter phase space distribution and decomposing it into moments, aiming to identify deviations from standard models.

Contribution

It presents a novel approach to extract the dark matter phase space distribution from directional data using integrals of motion and a model-independent basis decomposition.

Findings

Approximately 1000 events needed to detect deviations from the Standard Halo Model.

Method can infer global distribution properties under equilibrium conditions.

Illustrated using N-body simulation and analytical models.

Abstract

We discuss the relevance of directional detection experiments in the post-discovery era and propose a method to extract the local dark matter phase space distribution from directional data. The first feature of this method is a parameterization of the dark matter distribution function in terms of integrals of motion, which can be analytically extended to infer properties of the global distribution if certain equilibrium conditions hold. The second feature of our method is a decomposition of the distribution function in moments of a model independent basis, with minimal reliance on the ansatz for its functional form. We illustrate our method using the Via Lactea II N-body simulation as well as an analytical model for the dark matter halo. We conclude that O(1000) events are necessary to measure deviations from the Standard Halo Model and constrain or measure the presence of anisotropies.