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

TL;DR

This paper introduces an Elman Neural Network-based method for improved neutron and gamma-ray discrimination in liquid scintillator detectors, demonstrating superior performance over traditional neural networks in experimental tests.

Contribution

The paper presents a novel ENN-based discrimination method that enhances neutron/gamma separation performance in liquid scintillator detectors compared to existing neural network approaches.

Findings

ENN achieves higher FOM (0.953) than BPNN (0.907) in discrimination.

Experimental data from 241Am-9Be source validate the improved performance.

Proposes a new pulse shape discrimination technique for neutron detection.

Abstract

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.