摘要
中子探测中,由于存在非弹性散射和慢中子捕获等作用,形成了n/γ混合辐射场,增加了中子探测的复杂性。有机闪烁体因其闪烁效率高、衰减时间短、探测效率高被广泛应用于中子探测。脉冲形状甄别是根据有机闪烁体中粒子衰减时间不同引起的脉冲形状差异来甄别n/γ的关键技术。传统脉冲形状甄别方法包括时域和频域甄别方法;近年来,各种机器学习技术也相继应用于n/γ甄别,并取得较好效果。为了更好地使用有机闪烁体和n/γ甄别方法进行中子探测,我们从有机闪烁体的发光机理、脉冲形状甄别原理、有机闪烁体类型及n/γ甄别方法等方面进行了较为全面的分析和综述,并总结了有机闪烁体和n/γ甄别方法的各种性能评价指标。最后,对有机闪烁体和n/γ甄别方法的发展趋势提出了展望。
During the neutron detection process,owing to the effects of inelastic scattering and slow neutron capture,a neutron-gamma mixed radiation field is formed,which increases the complexity of neutron detection.Organic scintillators are widely used in neutron detection because of their high flashing efficiency,short decay time,and high detection efficiency.Pulse shape discrimination(PSD)is a key technology for discriminating neutrons and gamma rays according to the difference in pulse shape caused by the difference in particle decay time in organic scintillators.Traditional PSD methods include time-domain and frequency-domain discrimination methods.In recent years,various machine-learning techniques applied to neutron-gamma discrimination have achieved better results.To better use organic scintillators and the corresponding neutron-gamma discrimination methods in neutron detection,we conducted a comprehensive analysis of the glowing mechanism of organic scintillators,PSD principle,organic scintillator types,and neutron-gamma discrimination methods and investigated the performance evaluation indexes of organic scintillators and neutron-gamma discrimination methods.Finally,the future development directions of organic scintillators and neutron-gamma discrimination methods were examined.
作者
胡万平
张贵宇
张云龙
庹先国
HU Wanping;ZHANG Guiyu;ZHANG Yunlong;TUO Xianguo(School of Automation&Information Engineering,Sichuan University of Science&Engineering,Yibin 644000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science&Engineering,Yibin 644000,China)
出处
《核技术》
CAS
CSCD
北大核心
2023年第6期39-52,共14页
Nuclear Techniques
基金
国家自然科学基金(No.42004151)
四川轻化工大学研究生创新基金(No.Y2022117)资助。
关键词
中子探测
有机闪烁体
脉冲形状甄别
机器学习
品质因数
Neutron detection
Organic scintillators
PSD
Machine learning(ML)
Figure of merit(FOM)