n/γ discrimination for CLYC detector using a one-dimensional Convolutional Neural Network

In this work, a one-dimensional convolutional neural network (1D-CNN) is used for performing pulse shape discrimination (PSD) between the neutrons and gamma rays detected by the Cs₂LiYCl₆: Ce³⁺(CLYC) crystal. We use three different optimizers to train the CNN for comparing the effects of different optimizers on the training results. The neural network that uses the RMSProp optimizer performed the best. The accuracy of the AmBe source reaches 99.395%, and the false alarm rates (FARs) of the gamma source, i.e., ¹³⁷Cs and ²²Na are only 0.003% and 0.020%, separately. By the same dataset, we introduced several other methods to compare, including the classic charge integral (CI), partial charge-to-peak ratio (PCPR), decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN). Among these introduced methods, the FARs of the ANN method are better, which are 0.004% and 0.031%; however, its error is higher than that of the CNN method. A detailed discussion of the discrimination capability as a function of the sampling rate of the digitizer is also presented. We compare the performance of the CNN method and the traditional integral method under different sampling rates. The results show that even under a low sampling rate, the discrimination of the CNN method is almost unchanged, while the accuracy of the traditional integral method deteriorates rapidly. In addition, the CNN method is used for classifying more complicated particles, neutrons, gamma-rays, noise, and pile-up waveforms. The classification results show that the CNN has the ability to separate the four signals from the dataset efficiently.