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

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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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Authors and Affiliations

  1. School of Physics and State Key Laboratory of Nuclear Physics and Technology and Center for High Energy Physics, Peking University, Beijing, 100871, China

    Hao Qiao & SiGuang Wang

  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, 200240, China

    ChunYu Lu, Xun Chen, Ke Han & XiangDong Ji

  3. Tsung-Dao Lee Institute, Shanghai, 200240, China

    XiangDong Ji

Authors
  1. Hao Qiao
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  2. ChunYu Lu
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  3. Xun Chen
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  4. Ke Han
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  5. XiangDong Ji
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  6. SiGuang Wang
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Correspondence to Xun Chen or SiGuang Wang.

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Qiao, H., Lu, C., Chen, X. et al. Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation. Sci. China Phys. Mech. Astron. 61, 101007 (2018). https://doi.org/10.1007/s11433-018-9233-5

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  • DOI: https://doi.org/10.1007/s11433-018-9233-5

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