Detector signal characterization with a Bayesian network in XENONnT
Detector signal characterization with a Bayesian network in XENONnT
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DOI：
10.1103/physrevd.108.012016
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发表时间：
2023-04
影响因子：
5
通讯作者：
X. C. E. Aprile;K. Abe;S. A. Maouloud;L. Althueser;B. Andrieu;E. Angelino;J. Angevaare;V. C. Antochi;D. A. Martin;F. Arneodo;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. P. C. Chavez;A. Colijn;J. Conrad;J. Cuenca-Garc'ia;J. Cussonneau;V. D’Andrea;M. P. 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
中科院分区：
文献类型：
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作者：
X. C. E. Aprile;K. Abe;S. A. Maouloud;L. Althueser;B. Andrieu;E. Angelino;J. Angevaare;V. C. Antochi;D. A. Martin;F. Arneodo;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. P. C. Chavez;A. Colijn;J. Conrad;J. Cuenca-Garc'ia;J. Cussonneau;V. D’Andrea;M. P. 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
我们开发了一个基于贝叶斯网络的探测器信号表征模型，该模型基于双相氙气时间投影室产生的波形属性进行训练。通过对模型进行推理，我们产生了信号表征的定量度量，并证明该度量可用于确定探测器信号是来自闪烁还是电离过程。我们描述了该方法及其在XENONnT暗物质实验第一次科学运行中获得的电子反冲（ER）数据上的性能。我们演示了在基于波形的检测器信号分析中首次使用贝叶斯网络。这种方法导致了3%的ER事件选择效率的提高，同时有效地拒绝了感兴趣区域以外的事件。该分析结果与XENONnT先前的分析一致，即仅对ER数据进行背景拟合。
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.