Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c2 dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three Cryogenic Rare Event Search with Superconducting Thermometers (CRESST) detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.
Angloher, G., Banik, S., Benato, G., Bento, A., Bertolini, A., Breier, R., et al. (2024). Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning. COMPUTING AND SOFTWARE FOR BIG SCIENCE, 8(1) [10.1007/s41781-024-00119-y].
Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning
Canonica L.;Pattavina L.;
2024
Abstract
Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c2 dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three Cryogenic Rare Event Search with Superconducting Thermometers (CRESST) detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.