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Towards an automated data cleaning with deep learning in CRESST.
Angloher G, Banik S, Bartolot D, Benato G, Bento A, Bertolini A, Breier R, Bucci C, Burkhart J, Canonica L, D'Addabbo A, Di Lorenzo S, Einfalt L, Erb A, Feilitzsch FV, Iachellini NF, Fichtinger S, Fuchs D, Fuss A, Garai A, Ghete VM, Gerster S, Gorla P, Guillaumon PV, Gupta S, Hauff D, Ješkovský M, Jochum J, Kaznacheeva M, Kinast A, Kluck H, Kraus H, Lackner M, Langenkämper A, Mancuso M, Marini L, Meyer L, Mokina V, Nilima A, Olmi M, Ortmann T, Pagliarone C, Pattavina L, Petricca F, Potzel W, Povinec P, Pröbst F, Pucci F, Reindl F, Rizvanovic D, Rothe J, Schäffner K, Schieck J, Schmiedmayer D, Schönert S, Schwertner C, Stahlberg M, Stodolsky L, Strandhagen C, Strauss R, Usherov I, Wagner F, Willers M, Zema V, Waltenberger W; CRESST Collaboration. Angloher G, et al. Among authors: banik s. Eur Phys J Plus. 2023;138(1):100. doi: 10.1140/epjp/s13360-023-03674-2. Epub 2023 Jan 30. Eur Phys J Plus. 2023. PMID: 36741916 Free PMC article.
Secular equilibrium assessment in a CaWO4 target crystal from the dark matter experiment CRESST using Bayesian likelihood normalisation.
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, Feilitzsch FV, Ferreiro Iachellini N, Fichtinger S, Fuchs D, Fuss A, Garai A, Ghete VM, Gorla P, Gupta S, Hauff D, Ješkovský M, Jochum J, Kaznacheeva M, Kinast A, Kluck H, Kraus H, Langenkämper A, Mancuso M, Marini L, Mokina V, Nilima A, 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, Schmiedmayer D, Schönert S, Schwertner C, Stahlberg M, Stodolsky L, Strandhagen C, Strauss R, Usherov I, Wagner F, Willers M, Zema V; CRESST Collaboration; Ferella F, Laubenstein M, Nisi S. Angloher G, et al. Among authors: banik s. Appl Radiat Isot. 2023 Apr;194:110670. doi: 10.1016/j.apradiso.2023.110670. Epub 2023 Jan 21. Appl Radiat Isot. 2023. PMID: 36696751
Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning.
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, Waltenberger W. Angloher G, et al. Among authors: banik s. Comput Softw Big Sci. 2024;8(1):10. doi: 10.1007/s41781-024-00119-y. Epub 2024 May 22. Comput Softw Big Sci. 2024. PMID: 39539389 Free PMC article.
417 results