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.; Antón Martin, D.; 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-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.; 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.; Marrodán Undagoitia, T.; 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.; Ramírez García, D.; Singh, R.; Sanchez, L.; Dos Santos, J.  m.  f.; Sarnoff, I.; Sartorelli, G.; Schreiner, J.; Schulte, D.; Schulte, P.; Schulze Eißing, 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.; Tönnies, 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 waveformbased 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.