XENONnT 分析:信号重构、校准和事件选择

XENON Collaboration, E. Aprile, J. Aalbers, K. Abe, S. Ahmed Maouloud, L. Althueser, B. Andrieu, E. Angelino, J. R. Angevaare, D. Antón Martin, F. Arneodo, L. Baudis, M. Bazyk, L. Bellagamba, R. Biondi, A. Bismark, K. Boese, A. Brown, G. Bruno, R. Budnik, J. M. R. Cardoso, A. P. Cimental Chávez, A. P. Colijn, J. Conrad, J. J. Cuenca-García, V. D'Andrea, L. C. Daniel Garcia, M. P. Decowski, A. Deisting, C. Di Donato, P. Di Gangi, S. Diglio, K. Eitel, A. Elykov, A. D. Ferella, C. Ferrari, H. Fischer, T. Flehmke, M. Flierman, W. Fulgione, C. Fuselli, P. Gaemers, R. Gaior, M. Galloway, F. Gao, S. Ghosh, R. Giacomobono, R. Glade-Beucke, L. Grandi, J. Grigat, H. Guan, M. Guida, P. Gyoergy, R. Hammann, A. Higuera, C. Hils, L. Hoetzsch, N. F. Hood, M. Iacovacci, Y. Itow, J. Jakob, F. Joerg, Y. Kaminaga, M. Kara, P. Kavrigin, S. Kazama, M. Kobayashi, D. Koke, A. Kopec, F. Kuger, H. Landsman, R. F. Lang, L. Levinson, I. Li, S. Li, S. Liang, Y. -T. Lin, S. Lindemann, M. Lindner, K. Liu, J. Loizeau, F. Lombardi, J. Long, J. A. M. Lopes, T. Luce, Y. Ma, C. Macolino, J. Mahlstedt, A. Mancuso, L. Manenti, F. Marignetti, T. Marrodán Undagoitia, K. Martens, J. Masbou, E. Masson, S. Mastroianni, A. Melchiorre, J. Merz, M. Messina, A. Michael, K. Miuchi, A. Molinario, S. Moriyama, K. Morå, Y. Mosbacher, M. Murra, J. Müller, K. Ni, U. Oberlack, B. Paetsch, Y. Pan, Q. Pellegrini, R. Peres, C. Peters, J. Pienaar, M. Pierre, G. Plante, T. R. Pollmann, L. Principe, J. Qi, J. Qin, D. Ramírez García, M. Rajado, R. Singh, L. Sanchez, J. M. F. dos Santos, I. Sarnoff, G. Sartorelli, J. Schreiner, D. Schulte, P. Schulte, H. Schulze Eißing, M. Schumann, L. Scotto Lavina, M. Selvi, F. Semeria, P. Shagin, S. Shi, J. Shi, M. Silva, H. Simgen, A. Takeda, P. -L. Tan, A. Terliuk, D. Thers, F. Toschi, G. Trinchero, C. D. Tunnell, F. Tönnies, K. Valerius, S. Vecchi, S. Vetter, F. I. Villazon Solar, G. Volta, C. Weinheimer, M. Weiss, D. Wenz, C. Wittweg, V. H. S. Wu, Y. Xing, D. Xu, Z. Xu, M. Yamashita, L. Yang, J. Ye, L. Yuan, G. Zavattini, M. Zhong
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引用次数: 0

摘要

XENONnT 实验位于意大利大萨索国家实验室(INFN Laboratori Nazionali del GranSasso),设有一个 5.9 吨重的液态氙时间投影室,周围环绕着一个仪器式中子否决装置,所有这些装置都安装在氙否决水箱中。由于采用了广泛的屏蔽和先进的净化技术来消除天然放射性,在 TPC 内部,(1, 30) keV 区域的本底水平非常低,仅为 (15.8$\pm$ 1.3) 次/(吨/年) 。因此,XENONnT 对粒子物理标准模型内外与暗物质和中微子相互作用有关的各种罕见现象都很敏感,重点是以弱相互作用大质量粒子(WIMPs)的形式直接探测暗物质。从2021年5月到2021年12月,XENONnT在稀有搜索模式下积累数据,总曝光量为每年一吨。本文详细描述了信号重建方法、事件选择程序和探测器响应校准,并概述了这一时期的探测器性能。这项工作为我们在报告 XENONnT 物理结果时使用的 "盲分析 "方法建立了基础框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
XENONnT Analysis: Signal Reconstruction, Calibration and Event Selection
The XENONnT experiment, located at the INFN Laboratori Nazionali del Gran Sasso, Italy, features a 5.9 tonne liquid xenon time projection chamber surrounded by an instrumented neutron veto, all of which is housed within a muon veto water tank. Due to extensive shielding and advanced purification to mitigate natural radioactivity, an exceptionally low background level of (15.8 $\pm$ 1.3) events/(tonne$\cdot$year$\cdot$keV) in the (1, 30) keV region is reached in the inner part of the TPC. XENONnT is thus sensitive to a wide range of rare phenomena related to Dark Matter and Neutrino interactions, both within and beyond the Standard Model of particle physics, with a focus on the direct detection of Dark Matter in the form of weakly interacting massive particles (WIMPs). From May 2021 to December 2021, XENONnT accumulated data in rare-event search mode with a total exposure of one tonne $\cdot$ year. This paper provides a detailed description of the signal reconstruction methods, event selection procedure, and detector response calibration, as well as an overview of the detector performance in this time frame. This work establishes the foundational framework for the `blind analysis' methodology we are using when reporting XENONnT physics results.
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