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reconstruction of a muon bundle in the JUNO central detector

Nuclear Science and Techniques 2022年第5期33卷 78-90页

作者： Cheng-Feng Yang Yong-Bo Huang Ji-Lei Xu Di-Ru Wu Yong-Peng Zhang Wu-Ming Luo Miao He Guo-Ming Chen Si-Yuan Zhang School of Physical Science and Technology Guangxi UniversityNanning 530004China Institute of High Energy Physics Beijing 100049China University of Chinese Academy of Sciences Beijing 100049China

The Jiangmen Underground Neutrino Observatory(JUNO)is a multi-purpose neutrino experiment.One of the main goals is to determine the neutrino mass ordering by precisely measuring the energy spectrum of reactor antineutrinos.For the detection of reactor antineutrinos,cosmogenic backgrounds,such as^(9)Li/^(8)He and fast neutrons induced by cosmic muons,should be rejected carefully by applying muon veto cuts,which require good muon track reconstruction.With a 20-kton liquid scintillator detector,the simulation shows the proportion of muon bundles(muon multiplicity≥2)to be approximately 8%in JUNO,whereas its reconstruction has been rarely discussed in previous experiments.This study proposes an efficient algorithm for muon track reconstruction based on the charge response of a photomultiplier tube array.This is the first reconstruction of muon bundles in a large-volume liquid scintillator detector.In addition,the algorithm shows good performance and potential for reconstruction for both a single muon and double muons(muon multiplicity=2).The spatial resolution of a single-muon reconstruction was20 cm,and the angular resolution was 0.5°.For doublemuon reconstruction,the spatial and angular resolutions could be 30 cm and 1.0°,respectively.Moreover,this paper also discusses muon classification and the veto strategy.

关键词： JUNO Liquid scintillatordetector muon reconstruction muon bundle Veto strategy

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muon reconstruction with a convolutional neural network in the JUNO detector

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Radiation Detection Technology and Methods 2021年第3期5卷 364-372页

作者： Yan Liu Wei-Dong Li Tao Lin Wen-Xing Fang Simon C.Blyth Ji-Lei Xu Miao He Kun Zhang University of Chinese Academy of Sciences Beijing 100049China Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China

Purpose The Jiangmen Underground Neutrino Observatory(JUNO)is designed to determine the neutrino mass ordering and measure neutrino oscillation parameters.A precise muon reconstruction is crucial to reduce one of the major backgrounds induced by cosmic muons.Methods This article proposes a novel muon reconstruction method based on convolutional neural network(CNN)models.In this method,the track information reconstructed by the top tracker is used for network training.The training dataset is augmented by applying a rotation to muon tracks to compensate for the limited angular coverage of the top tracker.Result The muon reconstruction with the CNN model can produce unbiased tracks with performance that spatial resolution is better than 10 cm and angular resolution is better than 0.6◦.By using a GPU-accelerated implementation,a speedup factor of 100 compared to existing CPU techniques has been demonstrated.

关键词： JUNO muon reconstruction Convolutional neural networks GPU

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