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Nuclear Science and Techniques 2022年第7期33卷 93-102页

作者： Zi-Yuan Li Zhen Qian Jie-Han He Wei He Cheng-Xin Wu Xun-Ye Cai Zheng-Yun You Yu-Mei Zhang Wu-Ming Luo School of Physics Sun Yat-Sen UniversityGuangzhou510275China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen UniversityZhuhai519082China Institute of High Energy Physics Chinese Academy of SciencesBeijing100049China

The precise vertex reconstruction for large liquid scintillator detectors is essential.A novel machine learning-based method was successfully developed to reconstruct an event vertex in JUNO.In this study,the performance of machine learning-based vertex reconstruction was further improved by optimizing the input images of neural networks.By separating the information of different types of PMTs and adding the information of the second hit of PMTs,the vertex resolution was improved by approximately 9.4% at 1 MeV and 9.8% at 11 MeV.

关键词： JUNO liquid scintillator detector Neutrino experiment Vertex reconstruction Machine learning

Discrimination of pp solar neutrinos and^(14)C double pile-up events in a large-scale LS detector

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Nuclear Science and Techniques 2023年第9期34卷 69-81页

作者： Guo-Ming Chen Xin Zhang Ze-Yuan Yu Si-Yuan Zhang Yu Xu Wen-Jie Wu Yao-Guang Wang Yong-Bo Huang School of Physical Science and Technology Guangxi UniversityNanning 530004China Institute of High Energy Physics Beijing 100049China University of Chinese Academy of Sciences Beijing 100049China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Department of Physics and Astronomy University of CaliforniaIrvineCAUSA

As a unique probe,the precision measurement of pp solar neutrinos is important for studying the sun’s energy mechanism as it enables monitoring the thermodynamic equilibrium and studying neutrino oscillations in the vacuum-dominated region.For a large-scale liquid scintillator detector,a bottleneck for pp solar neutrino detection is the pile-up events of intrinsic14C decay.This paper presents a few approaches to discriminating between pp solar neutrinos and ^(14)C pile-up events by considering the differences in their time and spatial distributions.In this study,a Geant4-based Monte Carlo simulation is conducted.Multivariate analysis and deep learning technology are adopted to investigate the capability of ^(14)C pile-up reduction.The BDTG (boosted decision trees with gradient boosting) model and VGG network demonstrate good performance in discriminating pp solar neutrinos and ^(14)C double pile-up events.Under the ^(14)C concentration assumption of 5×10-18g/g,the signal significance can achieve 10.3 and 15.6 using the statistics of only one day.In this case,the signal efficiency for discrimination using the BDTG model while rejecting 99.18% ^(14)C double pile-up events is 51.1%,and that for the case where the VGG network is used while rejecting 99.81%of the ^(14)C double pile-up events is 42.7%.

关键词： liquid scintillator detector pp solar neutrinos 14C pile-up Multivariate analysis Deep learning

Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors

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Nuclear Science and Techniques 2023年第6期34卷 103-113页

作者： Gui-Hong Huang Wei Jiang Liang-Jian Wen Yi-Fang Wang Wu-Ming Luo School of Applied Physics and Materials Wuyi UniversityJiangmen529020China Institute of High Energy Physics Chinese Academy of SciencesBeijing100049China School of Physical Sciences University of Chinese Academy of ScienceBeijing100049China

High-precision vertex and energy reconstruction are crucial for large liquid scintillator detectors such as that at the Jiangmen Underground Neutrino Observatory(JUNO),especially for the determination of neutrino mass ordering by analyzing the energy spectrum of reactor neutrinos.This paper presents a data-driven method to obtain a more realistic and accurate expected PMT response of positron events in JUNO and develops a simultaneous vertex and energy reconstruction method that combines the charge and time information of PMTs.For the JUNO detector,the impact of the vertex inaccuracy on the energy resolution is approximately 0.6%.

关键词： JUNO liquid scintillator detector Neutrino experiment Vertex reconstruction Energy reconstruction

Event vertex and time reconstruction in large-volume liquid scintillator detectors

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Nuclear Science and Techniques 2021年第5期32卷 51-62页

作者： Zi-Yuan Li Yu-Mei Zhang Guo-Fu Cao Zi-Yan Deng Gui-Hong Huang Wei-Dong Li Tao Lin Liang-Jian Wen Miao Yu Jia-Heng Zou Wu-Ming Luo Zheng-Yun You School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China School of Physics and Technology Wuhan UniversityWuhan 430072China

Large-volume liquid scintillator detectors with ultra-low background levels have been widely used to study neutrino physics and search for dark matter.Event vertex and event time are not only useful for event selection but also essential for the reconstruction of event energy.In this study,four event vertex and event time reconstruction algorithms using charge and time information collected by photomultiplier tubes were analyzed comprehensively.The effects of photomultiplier tube properties were also investigated.The results indicate that the transit time spread is the main effect degrading the vertex reconstruction,while the effect of dark noise is limited.In addition,when the event is close to the detector boundary,the charge information provides better performance for vertex reconstruction than the time information.

关键词： JUNO liquid scintillator detector Neutrino experiment Vertex reconstruction Time reconstruction

JUNO sensitivity on proton decay p→νK^(+)searches

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Chinese Physics C 2023年第11期47卷 7-22页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram 安丰鹏安琪 Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Antonio Bergnoli Thilo Birkenfeld Sylvie Blin David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Mario Buscemi Jose Busto Ilya Butorov Anatael Cabrera Barbara Caccianiga 蔡浩蔡啸蔡严克蔡志岩 Riccardo Callegari Antonio Cammi Agustin Campeny 曹传亚曹国富曹俊 Rossella Caruso Cédric Cerna 常劲帆 Yun Chang 陈平平 Po-An Chen 陈少敏陈旭荣 Yi-Wen Chen 陈义学陈羽陈长程捷程雅苹 Alexey Chetverikov Davide Chiesa Pietro Chimenti Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Selma Conforti Di Lorenzo Daniele Corti Flavio Dal Corso Olivia Dalager Christophe De La Taille 邓智邓子艳 Wilfried Depnering Marco Diaz Xuefeng Ding 丁雅韵 Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko 董建蒙 Evgeny Doroshkevich Marcos Dracos Frédéric Druillole 杜然杜书先 Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Ulrike Fahrendholz 范东华樊磊方建方文兴 Marco Fargetta Dmitry Fedoseev Li-Cheng Feng 冯启春 Richard Ford Amélie Fournier 甘浩男 Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Agnese Giaz Nunzio Giudice Maxim Gonchar 龚光华宫辉 Yuri Gornushkin Alexandre Göttel Marco Grassi Christian Grewing Vasily Gromov 顾旻皓谷肖飞古宇关梦云 Nunzio Guardone Maria Gul 郭聪郭竞渊郭万磊郭新恒郭宇航 Paul Hackspacher Caren Hagner 韩然 Yang Han Muhammad Sohaib Hassan 何苗何伟 Tobias Heinz Patrick Hellmuth 衡月昆 Rafael Herrera 贺远强侯少静 Yee Hsiung Bei-Zhen Hu 胡航胡健润胡俊胡守扬胡涛胡宇翔胡焯钧黄春豪黄桂鸿黄翰雄黄文昊黄鑫黄性涛黄永波惠加琪霍雷霍文驹 Cédric Huss Safeer Hussain Ara Ioannisian Roberto Isocrate Beatrice Jelmini Kuo-Lun Jen Ignacio Jeria 季筱璐吉星曌贾慧慧贾俊基蹇司玉蒋荻蒋炜江晓山金如意荆小平 Cécile Jollet Jari Joutsenvaara Sirichok Jungthawan Leonidas Kalousis Philipp Ka Pontificia Universidad Católica de Chile SantiagoChile IPHC Universitéde StrasbourgCNRS/IN2P3F-67037 StrasbourgFrance Pakistan Institute of Nuclear Science and Technology IslamabadPakistan INFN Catania and Dipartimento di Fisica e Astronomia dell Universitàdi Catania CataniaItaly East China University of Science and Technology ShanghaiChina University of Science and Technology of China HefeiChina Joint Institute for Nuclear Research DubnaRussia INFN Sezione di Milano and Dipartimento di Fisica dell Universitàdi Milano MilanoItaly Department of Physics Faculty of ScienceChulalongkorn UniversityBangkokThailand IJCLab UniversitéParis-SaclayCNRS/IN2P391405 OrsayFrance Department of Physics and Earth Science University of Ferrara and INFN Sezione di FerraraFerraraItaly INFN Milano Bicocca and University of Milano Bicocca MilanoItaly INFN Sezione di Padova PadovaItaly III.Physikalisches Institut B RWTH Aachen UniversityAachenGermany Eberhard Karls Universität Tübingen Physikalisches InstitutTübingenGermany Institute of High Energy Physics BeijingChina SUBATECH Universitéde NantesIMT AtlantiqueCNRS-IN2P3NantesFrance Univ.Bordeaux CNRSLP2IUMR 5797F-33170 GradignanFrance Department of Physics National Taiwan UniversityTaipei Dipartimento di Fisica e Astronomia dell’Universitàdi Padova and INFN Sezione di Padova PadovaItaly University of Roma Tre and INFN Sezione Roma Tre RomaItaly Aix Marseille Univ CNRS/IN2P3CPPMMarseilleFrance Wuhan University WuhanChina INFN Milano Bicocca and Politecnico of Milano MilanoItaly National United University Miao-Li Dongguan University of Technology DongguanChina Tsinghua University BeijingChina Institute of Modern Physics Chinese Academy of SciencesLanzhouChina Institute of Physics National Yang Ming Chiao Tung UniversityHsinchu North China Electric Power University BeijingChina Sun Yat-Sen University GuangzhouChina Beijing Institute of Spacecraft Environment Engineering BeijingChina Universidade Estadual de Londrina LondrinaBrazil INFN Sez

The Jiangmen Underground Neutrino Observatory(JUNO)is a large liquid scintillator detector designed to explore many topics in fundamental physics.In this study,the potential of searching for proton decay in the p→νK^(+)mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification.Moreover,the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals.Based on these advantages,the detection efficiency for the proton decay via p→νK^(+)is 36.9%±4.9%with a background level of 0.2±0.05(syst)±0.2(stat)events after 10 years of data collection.The estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 years,which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.

关键词： proton decay grand unified theories JUNO liquid scintillator detector