Identification of Dark Matter with directional detection
  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.
