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

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Cai-Xun Zhang, Shin-Ted Lin, Jian-Ling Zhao, Xun-Zhen Yu, Li Wang, Jing-Jun Zhu and Hao-Yang Xing. Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network[J]. Chinese Physics C, 2016, 40(8): 086204. doi: 10.1088/1674-1137/40/8/086204
Cai-Xun Zhang, Shin-Ted Lin, Jian-Ling Zhao, Xun-Zhen Yu, Li Wang, Jing-Jun Zhu and Hao-Yang Xing. Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network[J]. Chinese Physics C, 2016, 40(8): 086204.  doi: 10.1088/1674-1137/40/8/086204 shu
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Received: 2015-09-23
Revised: 2016-03-30
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    Supported by National Natural Science Foundation of China (11275134,11475117)

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

    Corresponding author: Jing-Jun Zhu,
    Corresponding author: Hao-Yang Xing,
  • 1.  Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology,Sichuan 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
Fund Project:  Supported by National Natural Science Foundation of China (11275134,11475117)

Abstract: 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 ± 0.034 to 0.953 ± 0.037 by using the new method of the ENN. The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.

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