Discrimination of neutrons and {gamma}-rays in liquid scintillator based on Elman neural network

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.