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arxiv: 2508.03078 · v1 · pith:4AN6BO7Bnew · submitted 2025-08-05 · ⚛️ physics.ins-det · astro-ph.CO

Description of CRESST-II and CRESST-III pulse shape data

G. Angloher , S. Banik , D. Bartolot , G. Benato , A. Bento , A. Bertolini , R. Breier , C. Bucci
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J. Burkhart L. Canonica A. D'Addabbo S. Di Lorenzo L. Einfalt A. Erb F. v. Feilitzsch N. Ferreiro Iachellini S. Fichtinger D. Fuchs A. Fuss A. Garai V.M. Ghete P. Gorla P.V. Guillaumon S. Gupta D. Hauff M. Je\v{s}kovsk\'y J. Jochum M. Kaznacheeva A. Kinast H. Kluck S. Kuckuk H. Kraus M. Lackner A. Langenk\"amper M. Mancuso L. Marini L. Meyer V. Mokina P. Murali A. Nilima M. Olmi T. Ortmann C. Pagliarone L. Pattavina F. Petricca W. Potzel P. Povinec F. Pr\"obst F. Pucci F. Reindl D. Rizvanovic J. Rothe K. Sch\"affner J. Schieck D. Schmiedmayer S. Sch\"onert C. Schwertner M. Stahlberg L. Stodolsky C. Strandhagen R. Strauss I. Usherov F. Wagner M. Willers V. Zema (CRESST Collaboration)
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classification ⚛️ physics.ins-det astro-ph.CO
keywords datamodelsappliedavailablebinaryclassifierscleaningcollected
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A set of data from 68 cryogenic detectors operated in the CRESST dark matter search experiment between 2013 and 2019 was collected and labeled to train binary classifiers for data cleaning. Here, we describe the data set and how the trained models can be applied to new data. The data and models are available online.

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