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.; Null, Null

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.

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., et al.. - In: PHYSICAL REVIEW D. - ISSN 2470-0010. - 108:1(2023). [10.1103/physrevd.108.012016]