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.
@article{arxiv.2403.07053,
title = {Dark Matter-induced electron excitations in silicon and germanium with Deep Learning},
author = {Riccardo Catena and Einar Urdshals},
journal= {arXiv preprint arXiv:2403.07053},
year = {2024}
}