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Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation

  • Hao Qiao
  • ChunYu Lu
  • Xun Chen
  • Ke Han
  • XiangDong Ji
  • SiGuang Wang
Article

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.

Keywords

neutrino double beta decay convolutional neural networks background suppression 

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Physics and State Key Laboratory of Nuclear Physics and Technology and Center for High Energy PhysicsPeking UniversityBeijingChina
  2. 2.Institute of Particle and Nuclear Physics and School of Physics and Astronomy, Shanghai Jiao Tong UniversityShanghai Laboratory for Particle Physics and CosmologyShanghaiChina
  3. 3.Tsung-Dao Lee InstituteShanghaiChina

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