Statistical Issues in Astrophysical Searches for Particle Dark Matter
  In this review statistical issues appearing in astrophysical searches for
particle dark matter, i.e. indirect detection (dark matter annihilating into
standard model particles) or direct detection (dark matter particles scattering
in deep underground detectors) are discussed. One particular aspect of these
searches is the presence of very large uncertainties in nuisance parameters
(astrophysical factors) that are degenerate with parameters of interest (mass
and annihilation/decay cross sections for the particles). The likelihood
approach has become the most powerful tool, offering at least one well
motivated method for incorporation of nuisance parameters and increasing the
sensitivity of experiments by allowing a combination of targets superior to the
more traditional data stacking. Other statistical challenges appearing in
astrophysical searches are to large extent similar to any new physics search,
for example at colliders, a prime example being the calculation of trial
factors. Frequentist methods prevail for hypothesis testing and interval
estimation, Bayesian methods are used for assessment of nuisance parameters and
parameter estimation in complex parameter spaces. The basic statistical
concepts will be exposed, illustrated with concrete examples from experimental
searches and caveats will be pointed out.
