Earth-Scattering Induced Modulation in Low-Threshold Dark Matter Experiments
Xavier Bertou, Ansh Desai, Timon Emken, Rouven Essig, Tomer Volansky, Tien-Tien Yu
TL;DR
The paper investigates how strong DM interactions with ordinary matter can cause DM particles to scatter in the Earth, imprinting a daily sidereal modulation in low-threshold detectors targeting sub-GeV DM. It develops a dark-photon mediator benchmark and uses two transport tools, DaMaSCUS for full 3D Monte Carlo simulations and Verne as a faster analytic approach, to predict modulation patterns in silicon, xenon, and argon detectors located at SNOLAB (Northern Hemisphere) and SUPL (Southern Hemisphere). The results show that Earth-scattering can significantly modulate the flux and hence the event rate, with substantial modulation amplitudes near current experimental limits, especially for light mediators in the 1e− electron-ionization channel. These modulation signals can outperform rate-only searches when backgrounds dominate, and the authors provide open-source code and data to enable broader exploration of the MeV–GeV DM parameter space.
Abstract
Dark matter particles with sufficiently large interactions with ordinary matter can scatter in the Earth before reaching and scattering in a detector. This induces a modulation in the signal rate with a period of one sidereal day. We calculate this modulation for sub-GeV dark matter particles that interact either with a heavy or an ultralight dark-photon mediator and investigate the resulting signal in low-threshold detectors consisting of silicon, xenon, or argon targets. The scattering in the Earth is dominated by dark matter scatters off nuclei, while the signal in the detector is easiest to observe from dark matter scattering off electrons. We investigate the properties of the modulation signal and provide projections of the sensitivity of future experiments. We find that a search for a modulation signal can probe new regions of parameter space near the energy thresholds of current experiments, where the data are typically dominated by backgrounds.
