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 in | Chinese physics C Vol. 40; no. 8; pp. 130 - 135 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
01.08.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-1137 0254-3052 |
| DOI | 10.1088/1674-1137/40/8/086204 |
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| 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 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. |
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| AbstractList | In this work, a new neutron and gamma (n/ gamma ) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and gamma data were acquired from an EJ-335 LS detector, which was exposed in a super(241)Am- super(9)Be radiation field. Neutron and gamma 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/ gamma discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit (FOM) in n/ gamma discrimination. The FOM increases from 0.907 plus or minus 0.034 to 0.953 plus or minus 0.037 by using the new method of the ENN. The proposed n/ gamma discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection. 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. |
| Author | 张才勋 林兴德 赵建玲 余训臻 王力 朱敬军 幸浩洋 |
| AuthorAffiliation | Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610065, China School of Physical Science and Technology, Sichuan University, Chengdu 610065, China Key Laboratory of Particle and Radiation Imaging （Ministry of Education） and Department of Engineering Physics, Tsinghua University, Beijing 100084, China |
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| CitedBy_id | crossref_primary_10_2139_ssrn_4124621 crossref_primary_10_1109_TNS_2021_3103658 crossref_primary_10_1016_j_nima_2023_168025 crossref_primary_10_1016_j_net_2022_12_035 crossref_primary_10_1007_s41365_021_00915_w crossref_primary_10_1007_s10967_023_09327_z crossref_primary_10_1016_j_nima_2023_168492 crossref_primary_10_1007_s00170_022_10196_1 crossref_primary_10_1109_TNS_2019_2918439 crossref_primary_10_3390_app12052400 crossref_primary_10_1088_1748_0221_18_01_P01007 crossref_primary_10_1088_1748_0221_15_04_P04008 crossref_primary_10_1007_s41365_023_01305_0 crossref_primary_10_1016_j_matt_2022_10_010 |
| Cites_doi | 10.1016/S0168-9002(98)00654-8 10.1016/0029-554X(78)90746-2 10.1016/j.nima.2009.06.027 10.1016/0029-554X(59)90067-9 10.1016/j.nima.2008.09.028 10.1207/s15516709cog1402_1 10.1109/TNS.2010.2044246 10.1016/j.nima.2010.09.130 10.1016/j.astropartphys.2012.11.007 10.1016/0029-554X(61)90198-7 10.1016/S0168-9002(02)01063-X 10.1016/j.nima.2013.04.004 10.1016/S0927-6505(98)00012-7 10.1016/0029-554X(71)90054-1 10.1016/j.astropartphys.2004.07.005 10.1016/0029-554X(64)90333-7 10.1016/j.snb.2005.01.008 10.1016/j.nima.2014.12.087 10.1016/j.cpc.2011.02.008 |
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| Notes | 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 |
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| References | 22 23 24 25 Xing H. Y. (2) 2013; 37 Ding D. Z. (5) 2001 10 Esposito B. (15) 2004 11 12 13 14 Demuth H. (19) 16 17 18 1 3 4 6 7 8 9 20 21 |
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| SubjectTerms | 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 |
| Title | Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network |
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