Markov Chain Monte Carlo analysis to constrain Dark Matter properties with directional detection

TL;DR

This paper demonstrates how Markov Chain Monte Carlo methods can analyze directional dark matter detection data to simultaneously constrain WIMP properties and Galactic halo parameters, aiding in identifying non-baryonic dark matter.

Contribution

It introduces a novel MCMC analysis approach that jointly constrains WIMP and halo parameters from directional detection data, enhancing dark matter characterization.

Findings

Potential to constrain WIMP mass and cross section

Ability to estimate Galactic halo velocity dispersions

Supports identification of non-baryonic dark matter

Abstract

Directional detection is a promising dark matter search strategy. Indeed, weakly interacting massive particle (WIMP)-induced recoils would present a direction dependence toward the Cygnus constellation, while background-induced recoils exhibit an isotropic distribution in the Galactic rest frame. Taking advantage of these characteristic features and even in the presence of a sizeable background, it has recently been shown that data from forthcoming directional detectors could lead either to a competitive exclusion or to a conclusive discovery, depending on the value of the WIMP-nucleon cross section. However, it is possible to further exploit these upcoming data by using the strong dependence of the WIMP signal with : the WIMP mass and the local WIMP velocity distribution. Using a Markov chain Monte Carlo analysis of recoil events, we show for the first time the possibility to constrain the unknown WIMP parameters, both from particle physics (mass and cross section) and Galactic halo (velocity dispersion along the three axis), leading to an identification of non-baryonic dark matter.