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Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation
Authors:Hao Qiao  ChunYu Lu  Xun Chen  Ke Han  XiangDong Ji  SiGuang Wang
Affiliation:1.School of Physics and State Key Laboratory of Nuclear Physics and Technology and Center for High Energy Physics,Peking University,Beijing,China;2.Institute of Particle and Nuclear Physics and School of Physics and Astronomy, Shanghai Jiao Tong University,Shanghai Laboratory for Particle Physics and Cosmology,Shanghai,China;3.Tsung-Dao Lee Institute,Shanghai,China
Abstract:The PandaX-III experiment will search for neutrinoless double beta decay of 136Xe with high pressure gaseous time projection chambers at the China Jin-Ping underground Laboratory. The tracking feature of gaseous detectors helps suppress the background level, resulting in the improvement of the detection sensitivity. We study a method based on the convolutional neural networks to discriminate double beta decay signals against the background f r om high energy gammas generated by 214Bi and 208Tl decays based on detailed Monte Carlo simulation. Using the 2-dimensional projections of recorded tracks on two planes, the method successfully suppresses the background level by a factor larger than 100 with a high signal efficiency. An improvement of 62% on the efficiency ratio of (in_s/;sqrt { in b} ) is achieved in comparison with the baseline in the PandaX-III conceptual design report.
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