Detector signal characterization with a Bayesian network in XENONnT

Aprile, E.; Abe, K.; Ahmed Maouloud, S.; Althueser, L.; Andrieu, B.; Angelino, E.; Angevaare, J. ???R.; Antochi, V. ???C.; Martin, D. Ant??n.; 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.; Cimental Chavez, A. ???P.; Colijn, A. ???P.; Conrad, J.; Cuenca-Garc??a, 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.; Gallo Rosso, A.; 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. Marrod??n.; Martens, K.; Masbou, J.; Masson, D.; Masson, E.; Mastroianni, S.; Messina, M.; Miuchi, K.; Mizukoshi, K.; Molinario, A.; Moriyama, S.; Mor??, K.; Mosbacher, Y.; Murra, M.; M??ller, 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.; Ram??rez Garc??a, D.; Singh, R.; Sanchez, L.; dos Santos, J. ???M. ???F.; Sarnoff, I.; Sartorelli, G.; Schreiner, J.; Schulte, D.; Schulte, P.; Schulze Ei??ing, H.; Schumann, M.; Scotto Lavina, L.; 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.; T??nnies, F.; Valerius, K.; Volta, G.; Weinheimer, C.; Weiss, M.; Wenz, D.; Wittweg, C.; Wolf, T.; V. ???H. ???S., Wu; Xing, Y.; Xu, D.; Xu, Z.; Yamashita, M.; Yang, L.; Ye, J.; Yuan, L.; Zavattini, G.; Zhong, M.; Zhu, T.

doi:10.1103/physrevd.108.012016

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.