Prospects for Distinguishing Dark Matter Models Using Annual Modulation
Samuel J. Witte, Vera Gluscevic, and Samuel D. McDermott

TL;DR
This paper explores how analyzing the annual modulation of nuclear recoil rates in dark matter detection experiments can help distinguish between models with similar energy spectra, especially with larger exposures than current Generation 2 experiments.
Contribution
It demonstrates that annual modulation data can significantly improve dark matter model discrimination, even when recoil spectra are nearly degenerate, through Bayesian analysis.
Findings
Annual modulation enhances model distinguishability.
Large exposures improve identification accuracy.
Degeneracy can be broken with additional temporal information.
Abstract
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…
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