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

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 fi...

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Published inChinese physics C Vol. 40; no. 8; pp. 130 - 135
Main Author 张才勋 林兴德 赵建玲 余训臻 王力 朱敬军 幸浩洋
Format Journal Article
LanguageEnglish
Published 01.08.2016
Subjects
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
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ISSN1674-1137
0254-3052
DOI10.1088/1674-1137/40/8/086204

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Summary: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.
Bibliography: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
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ISSN:1674-1137
0254-3052
DOI:10.1088/1674-1137/40/8/086204