闪烁体
中子
中子探测
中子源
物理
散裂中子源
中子温度
核物理学
光学
探测器
作者
Da-Jun Zhao,Song Feng,Chuangye Hu,Zhenhua Zhang,Li Wang,Kun Zhu,Zijun Liu,Chaomin Chen,B. Zheng,Han‐Xiong Huang
标识
DOI:10.1016/j.radmeas.2022.106703
摘要
The FIssion Neutron spectrum Detector Array (FINDA) has been designed for measurement of prompt fission neutron spectrum on the Back-n beam line at the China Spallation Neutron Source (CSNS). In order to test the neutron- γ discrimination performance of the EJ301 scintillator that will be used as a detector unit in the FINDA, a comparative study using three conventional digital pulse shape discrimination (PSD) methods has been carried out. An Am–Be source has been used to generate the mixed neutron- γ radiation and the waveform data has been obtained with a fast digitizer. Pulse interpolation, baseline restoration and pulses pile-up rejection have been applied for digital pulse processing. Charge comparison (CC) method and simplified charge comparison(SCC) method show a better capability of neutron- γ discrimination than rise time comparison(RTC) method over the entire energy range. Rise time comparison method needs a very fast digitizer or pulse interpolation to improve the PSD ability. The analysis of neutron- γ discrimination performance of the EJ301 scintillator provides a strong technical support for fast neutron measurement using the FINDA at CSNS. • The neutron- γ discrimination performance of an EJ301 scintillator that will used as a detector unit in the FINDA spectrometer at CSNS has been characterized. • A comparative study using three conventional digital pulse shape discrimination (PSD) methods has been carried out. • Charge comparison (CC) method and simplified charge comparison (SCC) method show a better capability of neutron- γ discrimination than rise time comparison (RTC) method over the entire energy range. • With the current test we discussed the results in view of applications on the Back-n beam line at CSNS and the analysis provides a strong technical support for fast neutron measurement using the FINDA.
科研通智能强力驱动
Strongly Powered by AbleSci AI