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 241Am-9Be 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 {\pm} 0.034 to 0.953 {\pm} 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.
@article{arxiv.1509.06259,
title = {Discrimination of neutrons and {\gamma}-rays in liquid scintillator based on Elman neural network},
author = {Cai-Xun Zhang and Shin-Ted Lin and Jian-Ling Zhao and Li Wang and Xun-Zhen Yu and Jing-Jun Zhu and Hao-Yang Xing},
journal= {arXiv preprint arXiv:1509.06259},
year = {2016}
}
Comments
16 pages, 8 figures and 2 tables, version to appear in Chinese Physics C