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Detector signal characterization with a Bayesian network in XENONnT

XENON Collaboration; Aprile, E.; Abe, K.; Ahmed Maouloud, S.; Althueser, L.; Andrieu, B.; Angelino, E.; Angevaare, J. R.; Antochi, V. C.; Antón Martin, D.; Arneodo, F.; Baudis, L.; Baxter, A. L.; Bazyk, M.; Bellagamba, L.; Biondi, R.; Bismark, A.; Brookes, E. J.; Brown, A.; ... mehr

Abstract (englisch):

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.


Verlagsausgabe §
DOI: 10.5445/IR/1000160984
Veröffentlicht am 27.07.2023
Originalveröffentlichung
DOI: 10.1103/PhysRevD.108.012016
Scopus
Zitationen: 1
Web of Science
Zitationen: 2
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Astroteilchenphysik (IAP)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 26.07.2023
Sprache Englisch
Identifikator ISSN: 2470-0010, 2470-0029
KITopen-ID: 1000160984
HGF-Programm 51.13.01 (POF IV, LK 01) Neutrinophysik und Dunkle Materie
Erschienen in Physical Review D
Band 108
Heft 1
Seiten Art.-Nr.: 012016
Bemerkung zur Veröffentlichung Gefördert durch SCOAP3
Vorab online veröffentlicht am 11.04.2023
Nachgewiesen in Dimensions
arXiv
Web of Science
Scopus
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