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arxiv: 2304.05428 · v2 · pith:CXHKT4ATnew · submitted 2023-04-11 · ✦ hep-ex

Detector signal characterization with a Bayesian network in XENONnT

XENON Collaboration: E. Aprile , K. Abe , S. Ahmed Maouloud , L. Althueser , B. Andrieu , E. Angelino , J. R. Angevaare , V. C. Antochi
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D. Ant\'on Martin F. Arneodo L. Baudis A. L. Baxter M. Bazyk L. Bellagamba R. Biondi A. Bismark E. J. Brookes A. Brown S. Bruenner G. Bruno R. Budnik T. K. Bui C. Cai J. M. R. Cardoso D. Cichon A. P. Cimental Chavez A. P. Colijn J. Conrad J. J. Cuenca-Garc\'ia J. P. Cussonneau V. D'Andrea M. P. Decowski P. Di Gangi S. Di Pede S. Diglio K. Eitel A. Elykov S. Farrell A. D. Ferella C. Ferrari H. Fischer M. Flierman W. Fulgione C. Fuselli P. Gaemers R. Gaior A. Gallo Rosso M. Galloway F. Gao R. Glade-Beucke L. Grandi J. Grigat H. Guan M. Guida R. Hammann A. Higuera C. Hils L. Hoetzsch N. F. Hood J. Howlett M. Iacovacci Y. Itow J. Jakob F. Joerg A. Joy N. Kato M. Kara P. Kavrigin S. Kazama M. Kobayashi G. Koltman A. Kopec F. Kuger H. Landsman R. F. Lang L. Levinson I. Li S. Li S. Liang S. Lindemann M. Lindner K. Liu J. Loizeau F. Lombardi J. Long J. A. M. Lopes Y. Ma C. Macolino J. Mahlstedt A. Mancuso L. Manenti F. Marignetti T. Marrod\'an Undagoitia K. Martens J. Masbou D. Masson E. Masson S. Mastroianni M. Messina K. Miuchi K. Mizukoshi A. Molinario S. Moriyama K. Mor\r{a} Y. Mosbacher M. Murra J. M\"uller K. Ni U. Oberlack B. Paetsch J. Palacio Q. Pellegrini R. Peres C. Peters J. Pienaar M. Pierre V. Pizzella G. Plante T. R. Pollmann J. Qi J. Qin D. Ram\'irez Garc\'ia R. Singh L. Sanchez J. M. F. dos Santos I. Sarnoff G. Sartorelli J. Schreiner D. Schulte P. Schulte H. Schulze Ei{\ss}ing M. Schumann L. Scotto Lavina M. Selvi F. Semeria P. Shagin S. Shi E. Shockley M. Silva H. Simgen A. Takeda P.-L. Tan A. Terliuk D. Thers F. Toschi G. Trinchero C. Tunnell F. T\"onnies K. Valerius G. Volta C. Weinheimer M. Weiss D. Wenz C. Wittweg T. Wolf V. H. S. Wu Y. Xing D. Xu Z. Xu M. Yamashita L. Yang J. Ye L. Yuan G. Zavattini M. Zhong T. Zhu
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classification ✦ hep-ex
keywords detectorsignalanalysisbayesiancharacterizationnetworkxenonntdata
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We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is sourced from a scintillation or an ionization process. We describe the method and its performance on electronic-recoil (ER) data taken during the first science run of the XENONnT dark matter experiment. We demonstrate the first use of a Bayesian network in a waveform-based analysis of detector signals. This method resulted in a 3% increase in ER event-selection efficiency with a simultaneously effective rejection of events outside of the region of interest. The findings of this analysis are consistent with the previous analysis from XENONnT, namely a background-only fit of the ER data.

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