2024/03/11 by Riccardo Catena, Catena, Riccardo, Einar Urdshals +1
Biochemistry, Genetics and Molecular Biology · Materials Science · Physics and Astronomy · #Advanced Electron Microscopy Techniques and Applications #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Electron and X-Ray Spectroscopy Techniques #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Radiation Detection and Scintillator Technologies
paper · pdf · doi:10.48550/arxiv.2403.07053
openalex publication_date 2024/03/11 · openalex created_date 2024/03/14 · openalex updated_date 2026/07/28
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 \hrefhttps://github.com/urdshals/DEDDhere.