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 andγ data were acquired from an EJ-335 LS detector,which was exposed in a241Am-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/γdiscrimination 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 ± 0.034 to 0.953 ± 0.037 by using the new method of the ENN.The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.