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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 a ~(241)Am-~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.
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 and γ data were acquired from an EJ-335 LS detector, which was exposed in a ~ (241) Am- ~ 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. 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. proposed n / γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.