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Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning

G. Angloher,S. Banik,G. Benato,A. Bento,A. Bertolini,R. Breier,C. Bucci,J. Burkhart,L. Canonica,A. D’Addabbo,S. Di Lorenzo,L. Einfalt,A. Erb,F. v. Feilitzsch,S. Fichtinger,D. Fuchs,A. Garai,V. M. Ghete,P. Gorla,P. V. Guillaumon,S. Gupta,D. Hauff,M. Ješkovský,J. Jochum,M. Kaznacheeva,A. Kinast,S. Kuckuk,H. Kluck,H. Kraus,A. Langenkämper,M. Mancuso,L. Marini,B. Mauri,L. Meyer,V. Mokina, K. Niedermayer,M. Olmi,T. Ortmann,C. Pagliarone,L. Pattavina,F. Petricca,W. Potzel,P. Povinec,F. Pröbst,F. Pucci,F. Reindl,J. Rothe,K. Schäffner,J. Schieck,S. Schönert,C. Schwertner,M. Stahlberg,L. Stodolsky,C. Strandhagen,R. Strauss,I. Usherov,F. Wagner,V. Wagner,M. Willers,V. Zema, C. Heitzinger,W. Waltenberger

Computing and Software for Big Science（2024）

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关键词
Dark matter,Cryogenic calorimeter,Transition-edge sensor,Reinforcement learning
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