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arXiv · 2403.07053

Dark Matter-induced electron excitations in silicon and germanium with Deep Learning

Riccardo Catena·Einar Urdshals
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Abstract

We train a deep neural network (DNN) to output rates of dark matter (DM) induced electron excitations in silicon and germanium detectors. Our DNN provides a massive speedup of around $5$ orders of magnitude relative to existing methods (i.e. QEdark-EFT), allowing for extensive parameter scans in the event of an observed DM signal. The network is also lighter and simpler to use than alternative computational frameworks based on a direct calculation of the DM-induced excitation rate. The DNN can be downloaded $\href{https://github.com/urdshals/DEDD}{\text{here}}$.

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Riccardo Catena, Einar Urdshals. 2024-03-11. Dark Matter-induced electron excitations in silicon and germanium with Deep Learning. https://arxiv.org/abs/2403.07053

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