Ab initio calculations for DM-electron scattering rates: what level of theory is sufficient? A case study with silicon
ORAL
Abstract
As the search space for dark matter (DM) has shifted to sub-GeV DM candidate particles, increased attention has turned to solid state detectors built from quantum materials. While traditional solid state detector targets (e.g. Si or Ge) have been utilized in searches for dark matter for decades, more complex, anisotropic materials with narrow band gaps are promising for detecting sub-MeV dark matter through DM-electron scattering and absorption channels. Determining the DM-electron scattering rates in, and the sensitivity of detectors made of, these materials can be calculated from first principles with knowledge of the loss function, however the accuracy of these predictions is limited by the first-principles level of theory used to calculate the dielectric function.
Here, we show a case study on silicon to demonstrate how the expected sensitivity of such a detector varies with level of theory, using traditional Kohn-Sham density functional theory (DFT) calculations and incorporating self-energy corrections as implemented in the GW approximation. We compare results to other state-of-the-art codes and discuss implications for the DM field for detectors based on novel materials.
LA-UR-24-31339, PNNL-SA-205049
Here, we show a case study on silicon to demonstrate how the expected sensitivity of such a detector varies with level of theory, using traditional Kohn-Sham density functional theory (DFT) calculations and incorporating self-energy corrections as implemented in the GW approximation. We compare results to other state-of-the-art codes and discuss implications for the DM field for detectors based on novel materials.
LA-UR-24-31339, PNNL-SA-205049
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Publication: https://arxiv.org/abs/2310.00147
Presenters
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Samuel Linton Watkins
Pacific Northwest National Laboratory (PNNL)
Authors
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Samuel Linton Watkins
Pacific Northwest National Laboratory (PNNL)
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Elizabeth Peterson
Los Alamos National Laboratory (LANL)
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Christopher A Lane
Los Alamos National Lab, Los Alamos National Laboratory, Los Alamos National Laboratory (LANL)
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Jian-Xin Zhu
Los Alamos National Laboratory (LANL), Los Alamos National Laboratory