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Aprile, E.; Abe, K.; Ahmed Maouloud, S.; Angelino, E.; Angevaare, J. R.; Antón Martin, D.; Arneodo, F.; Baudis, L.; Bazyk, M.; Bismark, A.; Brookes, E. J.; Brown, A.; Bruenner, S.; Budnik, R.; Bui, T. K.; Cai, C.; Colijn, A. P.; Cussonneau, J. P.; Decowski, M. P.; Diglio, S.; Eitel, K.; Elykov, A.; Ferella, A. D.; Ferrari, C.; Fischer, H.; Flierman, M.; Fuselli, C.; Gallo Rosso, A.; Galloway, M.; Gao, F.; Glade-Beucke, R.; Grandi, L.; Hammann, R.; Higuera, A.; Hils, C.; Hoetzsch, L.; Iacovacci, M.; Itow, Y.; Jakob, J.; Joy, A.; Kara, M.; Kobayashi, M.; Koltman, G.; Kopec, A.; Kuger, F.; Landsman, H.; Liang, S.; Lindemann, S.; Lombardi, F.; Manenti, L.; Marignetti, F.; Marrodán Undagoitia, T.; Martens, K.; Masbou, J.; Masson, D.; Mastroianni, S.; Messina, M.; Miuchi, K.; Molinario, A.; Morå, K.; Mosbacher, Y.; Murra, M.; Müller, J.; Ni, K.; Oberlack, U.; Paetsch, B.; Palacio, J.; Peres, R.; Peters, C.; Pierre, M.; Plante, G.; Qi, J.; Qin, J.; Singh, R.; Sanchez, L.; dos Santos, J. M. F.; Sarnoff, I.; Schulte, P.; Schulze Eißing, H.; Scotto Lavina, L.; Selvi, M.; Semeria, F.; Shagin, P.; Shi, S.; Shockley, E.; Takeda, A.; Tan, P.-L.; Terliuk, A.; Toschi, F.; Trinchero, G.; Valerius, K.; Volta, G.; Wittweg, C.; Xing, Y.; Xu, Z.; Yang, L.; Ye, J.; Yuan, L.; Zavattini, G.; Zhu, T.
Physical review. D, 07/2023, Letnik: 108, Številka: 1Journal Article
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.
