Discrimination of neutrons and gamma rays in plastic scintillator based on pulse-coupled neural network

• 作者: Hao-Ran Liu Yu-Xin Cheng Zhuo Zuo Tian-Tian Sun Kai-Min Wang
• 来源期刊: 核技术（英文版）
• 年，卷（期）: 2021,（8）
• 所属期刊栏目: NUCLEAR ELECTRONICS AND INSTRUMENTATION
• 页码范围: 48-56
• 页数: 9页
• 语种: 英文

摘要

Neutron and gamma ray pulse signal discrimi-nation technology is an essential part of many modern scientific fields,such as biology,geology,radiation imag-ing,and nuclear medicine.Neutrons are always accompa-nied by gamma rays due to their unique penetration characteristic;thus,the development of n-γ discrimination methods is especially crucial.In the present study,a novel n-ydiscrimination method is proposed that implements a pulse-coupled neural network for n-γ discrimination.In addition,experiments were conducted on the pulse signals detected by an EJ299-33 plastic scintillator,which is especially suitable for n-γ discrimination.The proposed method was compared to three other discrimination meth-ods,including the back-propagation neural network(BPNN),the fractal spectrum method,and the charge comparison method,with respect to two aspects:(i)the figure of merit(FoM)and(ii)discrimination time.The experimental results showed that the pulse-coupled neural network(PCNN)has a 26.49％improvement in FoM-value compared to the charge comparison method,a 72.80％improvement compared to the BPNN,a 66.24％improvement compared to the fractal spectrum method,and the second-fastest discrimination time of 2.22 s.In conclusion,the PCNN treats the input signal as a whole for analysis and processing,imparting it with an excellent anti-noise effect and the ability to process the dynamic infor-mation contained in a pulse signal.