Identification of Dark Matter with directional detection

Abstract

Directional detection is a promising search strategy to discover galactic Dark Matter. Taking advantage on the rotation of the Solar system around the Galactic center through the Dark Matter halo, it allows to show a direction dependence of WIMP events. Data of directional detectors are composed of energy and a 3D track for each recoiling nuclei. Here, we present a Bayesian analysis method dedicated to data from upcoming directional detectors. However, we focus only on the angular part of the event distribution, arguing that the energy part of the background distribution is unknown. Two different cases are considered: a positive or a null detection of Dark Matter. In the first scenario, we will present a map-based likelihood method allowing to recover the main incoming direction of the signal and its significance, thus proving its Galactic origin. In the second scenario, a new statistical method is proposed. It is based on an extended likelihood in order to set robust and competitive exclusion limits. This method has been compared to two other methods and has been shown to be optimal in any detector configurations. Eventually, prospects for the MIMAC project are presented in the case of a 10 kg CF4 detector with an exposition time of 3 years.