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

description: 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.
description: 11 pages, 8 figures