Comparison of Artificial Intelligence Algorithms and Traditional Algorithms in Detector Neutron/Gamma Discrimination

Common neutron detectors are sensitive to neutrons and γ-rays, especially the organic scintillation detectors. Therefore, it is necessary to separate the two signals in neutron detection. In this paper, five algorithms by discriminating the pulse shape were implemented and compared, including charge comparison algorithm, risetime algorithm, frequency gradient analysis algorithm, K-means++ clustering algorithm and the feature extraction method of BP neural networks algorithm was optimized. The results show that the risetime algorithm is the best in terms of FOM, and the processing time based on BP neural network is the fastest, and the DER of BP neural network algorithm is most small. Based on the above work, this paper can provide a basis for discrimination and optimization of n/γ discrimination algorithms in practical work.