Exclusion, Discovery and Identification of Dark Matter with Directional
  Detection
  Directional detection is a promising search strategy to discover galactic
Dark Matter. We present a Bayesian analysis framework dedicated to data from
upcoming directional detectors. The interest of directional detection as a
powerful tool to set exclusion limits, to authentify a Dark Matter detection or
to constrain the Dark Matter properties, both from particle physics and
galactic halo physics, will be demonstrated.
