Adaptability of n–γ discrimination and filtering methods based on plastic scintillation

• Published in: Nuclear science and techniques Vol. 32; no. 3; pp. 63 - 71
• Main Authors: Zuo, Zhuo, Liu, Hao-Ran, Yan, Yu-Cheng, Liu, Bing-Qi, Zhang, Song
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01-03-2021; Chengdu University of Technology College of Engineering Technology, Leshan 614000, Sichuan, China; Southwest Institute of Physics, Chengdu 610225, China%Chengdu University of Technology College of Engineering Technology, Leshan 614000, Sichuan, China%Chengdu University, Chengdu 610106, China%Chengdu University of Technology, Chengdu 610225, China
• Subjects: Energy; Hadrons; Heavy Ions; Nuclear Energy; Nuclear Physics; Wavelet transform; Moving average; Kalman filter; Back-propagation neural network; Fourier transform; Charge comparison method; Fractal spectrum method
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-021-00865-3

Neutrons have been extensively used in many fields, such as nuclear physics, biology, geology, medical science, and national defense, owing to their unique penetration characteristics. Gamma rays are usually accompanied by the detection of neutrons. The capability to discriminate neutrons from gamma rays is important for evaluating plastic scintillator neutron detectors because similar pulse shapes are generated from both forms of radiation in the detection system. The pulse signals measured by plastic scintillators contain noise, which decreases the accuracy of n–
γ
discrimination. To improve the performance of n–
γ
discrimination, the noise of the pulse signals should be filtered before the n–
γ
discrimination process. In this study, the influences of the Fourier transform, wavelet transform, moving-average filter, and Kalman algorithm on the charge comparison method, fractal spectrum method, and back-propagation neural network methods were studied. It was found that the Fourier transform filtering algorithm exhibits better adaptability to the charge comparison method than others, with an increasing accuracy of 6.87% compared to that without the filtering process. Meanwhile, the Kalman filter offers an improvement of 3.04% over the fractal spectrum method, and the adaptability of the moving-average filter in back-propagation neural network discrimination is better than that in other methods, with an increase in 8.48%. The Kalman filtering algorithm has a significant impact on the peak value of the pulse, reaching 4.49%, and it has an insignificant impact on the energy resolution of the spectrum measurement after discrimination.