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）
Key words
Dark matter,Cryogenic calorimeter,Transition-edge sensor,Reinforcement learning
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