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It has recently been demonstrated that, in the event of a putative signal indark matter direct detection experiments, properly identifying the underlyingdark matter-nuclei interaction promises to be a challenging task. Given themost optimistic expectations for the number counts of recoil events in theforthcoming Generation 2 experiments, differentiating between interactions thatproduce distinct features in the recoil energy spectra will only be possible ifa strong signal is observed simultaneously on a variety of complementarytargets. However, there is a wide range of viable theories that give rise tovirtually identical energy spectra, and may only differ by the dependence ofthe recoil rate on the dark matter velocity. In this work, we investigate howdegeneracy between such competing models may be broken by analyzing the timedependence of nuclear recoils, i.e. the annual modulation of the rate. For thispurpose, we simulate dark matter events for a variety of interactions andexperiments, and perform a Bayesian model-selection analysis on all simulateddata sets, evaluating the chance of correctly identifying the input model for agiven experimental setup. We find that including information on the annualmodulation of the rate may significantly enhance the ability of a single targetto distinguish dark matter models with nearly degenerate recoil spectra, butonly with exposures beyond the expectations of Generation 2 experiments.

Prospects for Distinguishing Dark Matter Models Using Annual Modulation