학술논문
Detector signal characterization with a Bayesian network in XENONnT
Document Type
Working Paper
Author
XENON Collaboration; Aprile, E.; Abe, K.; Maouloud, S. Ahmed; Althueser, L.; Andrieu, B.; Angelino, E.; Angevaare, J. R.; Antochi, V. C.; Martin, D. Antón; Arneodo, F.; Baudis, L.; Baxter, A. L.; Bazyk, M.; Bellagamba, L.; Biondi, R.; Bismark, A.; Brookes, E. J.; Brown, A.; Bruenner, S.; Bruno, G.; Budnik, R.; Bui, T. K.; Cai, C.; Cardoso, J. M. R.; Cichon, D.; Chavez, A. P. Cimental; Colijn, A. P.; Conrad, J.; Cuenca-García, J. J.; Cussonneau, J. P.; D'Andrea, V.; Decowski, M. P.; Di Gangi, P.; Di Pede, S.; Diglio, S.; Eitel, K.; Elykov, A.; Farrell, S.; Ferella, A. D.; Ferrari, C.; Fischer, H.; Flierman, M.; Fulgione, W.; Fuselli, C.; Gaemers, P.; Gaior, R.; Rosso, A. Gallo; Galloway, M.; Gao, F.; Glade-Beucke, R.; Grandi, L.; Grigat, J.; Guan, H.; Guida, M.; Hammann, R.; Higuera, A.; Hils, C.; Hoetzsch, L.; Hood, N. F.; Howlett, J.; Iacovacci, M.; Itow, Y.; Jakob, J.; Joerg, F.; Joy, A.; Kato, N.; Kara, M.; Kavrigin, P.; Kazama, S.; Kobayashi, M.; Koltman, G.; Kopec, A.; Kuger, F.; Landsman, H.; Lang, R. F.; Levinson, L.; Li, I.; Li, S.; Liang, S.; Lindemann, S.; Lindner, M.; Liu, K.; Loizeau, J.; Lombardi, F.; Long, J.; Lopes, J. A. M.; Ma, Y.; Macolino, C.; Mahlstedt, J.; Mancuso, A.; Manenti, L.; Marignetti, F.; Undagoitia, T. Marrodán; Martens, K.; Masbou, J.; Masson, D.; Masson, E.; Mastroianni, S.; Messina, M.; Miuchi, K.; Mizukoshi, K.; Molinario, A.; Moriyama, S.; Morå, K.; Mosbacher, Y.; Murra, M.; Müller, J.; Ni, K.; Oberlack, U.; Paetsch, B.; Palacio, J.; Pellegrini, Q.; Peres, R.; Peters, C.; Pienaar, J.; Pierre, M.; Pizzella, V.; Plante, G.; Pollmann, T. R.; Qi, J.; Qin, J.; García, D. Ramírez; Singh, R.; Sanchez, L.; Santos, J. M. F. dos; Sarnoff, I.; Sartorelli, G.; Schreiner, J.; Schulte, D.; Schulte, P.; Eißing, H. Schulze; Schumann, M.; Lavina, L. Scotto; Selvi, M.; Semeria, F.; Shagin, P.; Shi, S.; Shockley, E.; Silva, M.; Simgen, H.; Takeda, A.; Tan, P. -L.; Terliuk, A.; Thers, D.; Toschi, F.; Trinchero, G.; Tunnell, C.; Tönnies, F.; Valerius, K.; Volta, G.; Weinheimer, C.; Weiss, M.; Wenz, D.; Wittweg, C.; Wolf, T.; Wu, V. H. S.; Xing, Y.; Xu, D.; Xu, Z.; Yamashita, M.; Yang, L.; Ye, J.; Yuan, L.; Zavattini, G.; Zhong, M.; Zhu, T.
Source
Phys. Rev. D 108, 012016 (2023)
Subject
Language
Abstract
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.
Comment: 11 pages, 8 figures
Comment: 11 pages, 8 figures