Muon reconstruction with a convolutional neural network in the JUNO detector

作     者：Yan Liu Wei-Dong Li Tao Lin Wen-Xing Fang Simon C.Blyth Ji-Lei Xu Miao He Kun Zhang 

作者机构：University of Chinese Academy of SciencesBeijing 100049China Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China 

出 版 物：《Radiation Detection Technology and Methods》 (辐射探测技术与方法（英文）)

年 卷 期：2021年第5卷第3期

页      面：364-372页

核心收录：

学科分类：07[理学] 0807[工学-动力工程及工程热物理] 0827[工学-核科学与技术] 0703[理学-化学] 070202[理学-粒子物理与原子核物理] 0702[理学-物理学] 0801[工学-力学（可授工学、理学学位）] 

基　　金：Supported by Strategic Priority Research Program of Chinese Academy of Sciences(XDA10010900) NSFC(11805223) 

主　　题：JUNO Muon reconstruction Convolutional neural networks GPU 

摘      要：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 *** This article proposes a novel muon reconstruction method based on convolutional neural network(CNN)*** this method,the track information reconstructed by the top tracker is used for network *** training dataset is augmented by applying a rotation to muon tracks to compensate for the limited angular coverage of the top *** 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.