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High-Dimensional Bayesian Likelihood Normalisation for CRESST's Background Model

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. Jeskovsky,J. Jochum,M. Kaznacheeva,A. Kinast,H. Kluck,H. Kraus,S. Kuckuk,A. Langenkaemper,M. Mancuso,L. Marini,L. Meyer,V. Mokina,A. Nilima,M. Olmi,T. Ortmann,C. Pagliarone,L. Pattavina,F. Petricca,W. Potzel,P. Povinec,F. Proebst,F. Pucci,F. Reindl,J. Rothe,K. Schaeffner,J. Schieck,D. Schmiedmayer,S. Schoenert,C. Schwertner,M. Stahlberg,L. Stodolsky,C. Strandhagen,R. Strauss,I. Usherov,F. Wagner,M. Willers,V. Zema,F. Ferella,M. Laubenstein,S. Nisi

JOURNAL OF INSTRUMENTATION（2024）

Cited 0|Views33
Key words
Analysis and statistical methods,Data processing methods,Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter etc),Simulation methods and programs
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