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 Am-241-Be-9 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 +/- 0.034 to 0.953 +/- 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.