Dark matter-induced electron excitations in silicon and germanium with deep learning

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., dark-), 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. Published by the American Physical Society 2025