Prospects for Distinguishing Dark Matter Models Using Annual Modulation
  It has recently been demonstrated that, in the event of a putative signal in
dark matter direct detection experiments, properly identifying the underlying
dark matter-nuclei interaction promises to be a challenging task. Given the
most optimistic expectations for the number counts of recoil events in the
forthcoming Generation 2 experiments, differentiating between interactions that
produce distinct features in the recoil energy spectra will only be possible if
a strong signal is observed simultaneously on a variety of complementary
targets. However, there is a wide range of viable theories that give rise to
virtually identical energy spectra, and may only differ by the dependence of
the recoil rate on the dark matter velocity. In this work, we investigate how
degeneracy between such competing models may be broken by analyzing the time
dependence of nuclear recoils, i.e. the annual modulation of the rate. For this
purpose, we simulate dark matter events for a variety of interactions and
experiments, and perform a Bayesian model-selection analysis on all simulated
data sets, evaluating the chance of correctly identifying the input model for a
given experimental setup. We find that including information on the annual
modulation of the rate may significantly enhance the ability of a single target
to distinguish dark matter models with nearly degenerate recoil spectra, but
only with exposures beyond the expectations of Generation 2 experiments.
