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Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network

张才勋 林兴德 赵建玲 余训臻 王力 朱敬军 幸浩洋

Chinese physics C, 2016-08, Vol.40 (8), p.130-135 [Periódico revisado por pares]

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  • Título:
    Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network
  • Autor: 张才勋 林兴德 赵建玲 余训臻 王力 朱敬军 幸浩洋
  • Assuntos: Artificial neural networks ; Back propagation ; BP神经网络 ; Data acquisition ; Detectors ; Discrimination ; Elman神经网络 ; Liquids ; Neural networks ; Pulse shape ; 中子探测 ; 人工神经网络 ; 光芒 ; 反向传播神经网络 ; 液体闪烁探测器 ; 脉冲形状甄别
  • É parte de: Chinese physics C, 2016-08, Vol.40 (8), p.130-135
  • Notas: In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network （BPNN） as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit （FOM） in n/γ discrimination. The FOM increases from 0.907 4- 0.034 to 0.953 4- 0.037 by using the new method of the ENN. The proposed n/γdiscrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.
    11-5641/O4
    liquid scintillator, n/γ discrimination, Elman neural network, BP neural network
    Cai-Xun Zhang, Shin-Ted Lin, Jian-Ling Zhao, Xun-Zhen Yu, Li Wang, Jing-Jun Zhu,Hao-Yang Xing（Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology Siehuan University, Chengdu 610065, China 2 School of Physical Science and Technology, Sichuan University, Chengdu 610065, China 3 Key Laboratory of Particle and Radiation Imaging （Ministry of Education） and Department of Engineering Physics, Tsinghua University, Beijing 100084, China）
    ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network （BPNN） as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit （FOM） in n/γ discrimination. The FOM increases from 0.907 4- 0.034 to 0.953 4- 0.037 by using the new method of the ENN. The proposed n/γdiscrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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