Detector signal characterization with a Bayesian network in XENONnT

/ Authors

X. C. E. Aprile, K. Abe, S. A. Maouloud, L. Althueser, B. Andrieu, E. Angelino, J. Angevaare, V. C. Antochi, D. Martin, F. Arneodo

and 156 more authors

L. Baudis, A. Baxter, M. Bazyk, L. Bellagamba, R. Biondi, A. Bismark, E. J. Brookes, A. Brown, S. Bruenner, G. Bruno, R. Budnik, T. Bui, C. Cai, J. Cardoso, D. Cichon, A. Chavez, A. Colijn, J. Conrad, J. Cuenca-Garc'ia, J. Cussonneau, V. D’Andrea, M. Decowski, P. Gangi, S. D. Pede, S. Diglio, K. Eitel, A. Elykov, S. Farrell, A. Ferella, C. Ferrari, H. Fischer, M. Flierman, W. Fulgione, C. Fuselli, P. Gaemers, R. Gaior, A. G. Rosso, M. Galloway, F. Gao, R. Glade-Beucke, L. Grandi, J. Grigat, H. Guan, M. Guida, R. Hammann, A. Higuera, C. Hils, L. Hoetzsch, N. Hood, J. Howlett, M. Iacovacci, Y. Itow, J. Jakob, F. Joerg, A. Joy, N. Kato, M. Kara, P. Kavrigin, S. Kazama, M. Kobayashi, G. Koltman, A. Kopec, F. Kuger, H. Landsman, R. Lang, L. Levinson, I. Li, S. Li, S. Liang, S. Lindemann, M. Lindner, K. Liu, J. Loizeau, F. Lombardi, J. Long, J. Lopes, Y. Ma, C. Macolino, J. Mahlstedt, A. Mancuso, L. Manenti, F. Marignetti, T. Undagoitia, K. Martens, J. Masbou, D. Masson, E. Masson, S. Mastroianni, M. Messina, K. Miuchi, K. Mizukoshi, A. Molinario, S. Moriyama, K. Morra, Y. Mosbacher, M. Murra, J. Muller, K. Ni, U. Oberlack, B. Paetsch, J. Palacio, Q. Pellegrini, R. Peres, C. Peters, J. Pienaar, M. Pierre, V. Pizzella, G. Plante, T. Pollmann, J. Qi, J. Qin, D. R. Garc'ia, R. Singh, L. Sanchez, J. Santos, I. Sarnoff, G. Sartorelli, J. Schreiner, D. Schulte, P. Schulte, H. Eißing, M. Schumann, L. Lavina, M. Selvi, F. Semeria, P. Shagin, S. Shi, E. Shockley, M. Silva, H. Simgen, A. Takeda, P. Tan, A. Terliuk, D. Thers, F. Toschi, G. Trinchero, C. Tunnell, F. Tonnies, K. Valerius, G. Volta, C. Weinheimer, M. Weiss, D. Wenz, C. Wittweg, Thomas Wolf, V. Wu, Y. Xing, D. Xu, Z. Xu, M. Yamashita, L. Yang, J. Ye, L. Yuan, G. Zavattini, M. Zhong, T. Zhu

/ Abstract

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.

Journal: Physical Review D

DOI: 10.1103/PhysRevD.108.012016