Detector signal characterization with a Bayesian network in XENONnT
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.
Cite
@article{arxiv.2304.05428,
title = {Detector signal characterization with a Bayesian network in XENONnT},
author = {XENON Collaboration and E. Aprile and K. Abe and S. Ahmed Maouloud and L. Althueser and B. Andrieu and E. Angelino and J. R. Angevaare and V. C. Antochi and D. Antón Martin and F. Arneodo and L. Baudis and A. L. Baxter and M. Bazyk and L. Bellagamba and R. Biondi and A. Bismark and E. J. Brookes and A. Brown and S. Bruenner and G. Bruno and R. Budnik and T. K. Bui and C. Cai and J. M. R. Cardoso and D. Cichon and A. P. Cimental Chavez and A. P. Colijn and J. Conrad and J. J. Cuenca-García and J. P. Cussonneau and V. D'Andrea and M. P. Decowski and P. Di Gangi and S. Di Pede and S. Diglio and K. Eitel and A. Elykov and S. Farrell and A. D. Ferella and C. Ferrari and H. Fischer and M. Flierman and W. Fulgione and C. Fuselli and P. Gaemers and R. Gaior and A. Gallo Rosso and M. Galloway and F. Gao and R. Glade-Beucke and L. Grandi and J. Grigat and H. Guan and M. Guida and R. Hammann and A. Higuera and C. Hils and L. Hoetzsch and N. F. Hood and J. Howlett and M. Iacovacci and Y. Itow and J. Jakob and F. Joerg and A. Joy and N. Kato and M. Kara and P. Kavrigin and S. Kazama and M. Kobayashi and G. Koltman and A. Kopec and F. Kuger and H. Landsman and R. F. Lang and L. Levinson and I. Li and S. Li and S. Liang and S. Lindemann and M. Lindner and K. Liu and J. Loizeau and F. Lombardi and J. Long and J. A. M. Lopes and Y. Ma and C. Macolino and J. Mahlstedt and A. Mancuso and L. Manenti and F. Marignetti and T. Marrodán Undagoitia and K. Martens and J. Masbou and D. Masson and E. Masson and S. Mastroianni and M. Messina and K. Miuchi and K. Mizukoshi and A. Molinario and S. Moriyama and K. Morå and Y. Mosbacher and M. Murra and J. Müller and K. Ni and U. Oberlack and B. Paetsch and J. Palacio and Q. Pellegrini and R. Peres and C. Peters and J. Pienaar and M. Pierre and V. Pizzella and G. Plante and T. R. Pollmann and J. Qi and J. Qin and D. Ramírez García and R. Singh and L. Sanchez and J. M. F. dos Santos and I. Sarnoff and G. Sartorelli and J. Schreiner and D. Schulte and P. Schulte and H. Schulze Eißing and M. Schumann and L. Scotto Lavina and M. Selvi and F. Semeria and P. Shagin and S. Shi and E. Shockley and M. Silva and H. Simgen and A. Takeda and P. -L. Tan and A. Terliuk and D. Thers and F. Toschi and G. Trinchero and C. Tunnell and F. Tönnies and K. Valerius and G. Volta and C. Weinheimer and M. Weiss and D. Wenz and C. Wittweg and T. Wolf and V. H. S. Wu and Y. Xing and D. Xu and Z. Xu and M. Yamashita and L. Yang and J. Ye and L. Yuan and G. Zavattini and M. Zhong and T. Zhu},
journal= {arXiv preprint arXiv:2304.05428},
year = {2023}
}
Comments
11 pages, 8 figures