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Improved fast neutron detection using CNN-based pulse shape discrimination

Nuclear Engineering and Technology, 2023, 55(11), , pp.3925-3934 [Peer Reviewed Journal]

2023 Korean Nuclear Society ;ISSN: 1738-5733 ;EISSN: 2234-358X ;DOI: 10.1016/j.net.2023.07.007

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  • Title:
    Improved fast neutron detection using CNN-based pulse shape discrimination
  • Author: Yoon, Seonkwang ; Lee, Chaehun ; Seo, Hee ; Kim, Ho-Dong
  • Subjects: Charge comparison method ; Convolution neural network ; Energy dependency ; Fast neutron detection ; Organic scintillator ; Pulse-shape discrimination ; 원자력공학
  • Is Part Of: Nuclear Engineering and Technology, 2023, 55(11), , pp.3925-3934
  • Description: The importance of fast neutron detection for nuclear safeguards purposes has increased due to its potential advantages such as reasonable cost and higher precision for larger sample masses of nuclear materials. Pulse-shape discrimination (PSD) is inevitably used to discriminate neutron- and gamma-ray- induced signals from organic scintillators of very high gamma sensitivity. The light output (LO) threshold corresponding to several MeV of recoiled proton energy could be necessary to achieve fine PSD performance. However, this leads to neutron count losses and possible distortion of results obtained by neutron multiplicity counting (NMC)-based nuclear material accountancy (NMA). Moreover, conventional PSD techniques are not effective for counting of neutrons in a high-gamma-ray environment, even under a sufficiently high LO threshold. In the present work, PSD performance (figure-of-merit, FOM) according to LO bands was confirmed using a conventional charge comparison method (CCM) and compared with results obtained by convolution neural network (CNN)-based PSD algorithms. Also, it was attempted, for the first time ever, to reject fake neutron signals from distorted PSD regions where neutron-induced signals are normally detected. The overall results indicated that higher neutron detection efficiency with better accuracy could be achieved via CNN-based PSD algorithms.
  • Publisher: Elsevier B.V
  • Language: English;Korean
  • Identifier: ISSN: 1738-5733
    EISSN: 2234-358X
    DOI: 10.1016/j.net.2023.07.007
  • Source: Alma/SFX Local Collection
    DOAJ Directory of Open Access Journals
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