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

作     者：张才勋 林兴德 赵建玲 余训臻 王力 朱敬军 幸浩洋 

作者机构：Key Laboratory of Radiation Physics and Technology of Ministry of EducationInstitute of Nuclear Science and TechnologySichuan University School of Physical Science and TechnologySichuan University Key Laboratory of Particle and Radiation Imaging(Ministry of Education) and Department of Engineering PhysicsTsinghua University 

出 版 物：《Chinese Physics C》 (中国物理C（英文版）)

年 卷 期：2016年第40卷第8期

页      面：130-135页

核心收录：

学科分类：12[管理学] 0709[理学-地质学] 08[工学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0708[理学-地球物理学] 0804[工学-仪器科学与技术] 0827[工学-核科学与技术] 082701[工学-核能科学与工程] 0703[理学-化学] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by National Natural Science Foundation of China(11275134 11475117) 

主　　题：liquid scintillator n/γ discrimination Elman neural network BP neural network 

摘      要：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.