1. Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning.
- Author
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Angloher G, Banik S, Benato G, Bento A, Bertolini A, Breier R, Bucci C, Burkhart J, Canonica L, D'Addabbo A, Di Lorenzo S, Einfalt L, Erb A, V Feilitzsch F, Fichtinger S, Fuchs D, Garai A, Ghete VM, Gorla P, Guillaumon PV, Gupta S, Hauff D, Ješkovský M, Jochum J, Kaznacheeva M, Kinast A, Kuckuk S, Kluck H, Kraus H, Langenkämper A, Mancuso M, Marini L, Mauri B, Meyer L, Mokina V, Niedermayer K, Olmi M, Ortmann T, Pagliarone C, Pattavina L, Petricca F, Potzel W, Povinec P, Pröbst F, Pucci F, Reindl F, Rothe J, Schäffner K, Schieck J, Schönert S, Schwertner C, Stahlberg M, Stodolsky L, Strandhagen C, Strauss R, Usherov I, Wagner F, Wagner V, Willers M, Zema V, Heitzinger C, and Waltenberger W
- Abstract
Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c 2 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., Competing Interests: Competing interestsOn behalf of all authors, the corresponding author states that there is no conflict of interest. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request., (© The Author(s) 2024.)
- Published
- 2024
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