Discrimination of piled-up neutron-gamma pulses using charge comparison method and neural network for CLYC detectors

To realize neutron-gamma discrimination of piled-up pulses in high counting rate circumstances, the discrim-ination performance of the charge comparison method and three neural network models to actual piled-up pulses was studied in this paper. The neural network models, including a residual neural network (ResNet), a convolutional neural network (CNN) and a fully connected neural network (FCNN), were trained and tested by seven kinds of actual experimental data sets, which included the background signals (bg), non-piled-up neutron (n) and gamma (g) pulses as well as piled-up neutron + neutron (n + n), neutron + gamma (n + g), gamma + neutron (g + n), and gamma + gamma (g + g) pulses. The labels of the seven data sets were provided after discriminating by the charge comparison method. The results showed that the charge comparison method could be applied to discriminate piled-up neutron-gamma pulses with the Figure of Merit (FoM) value of 1.0. The integration length has been optimized to be different for piled-up and non-piled-up pulses, but follow the same criterion for neutron and gamma discrimination. The FoM values are worse than that of non-piled-up signals because the baseline fluctuation is very large under high counting rate conditions, which gives lots of interference to pulse shape discrimination. The ResNet model has the highest total prediction accuracy (93.85%) for the seven signal types, and through the comparison and analysis of the inconsistent events discriminated by the charge comparison method and the ResNet model, it is concluded that the discrimination result of the ResNet model is more accurate. These results indicate that both the charge comparison method and residual neural network can be used for complicated n/? discrimination under high counting rate conditions, and the residual neural network works better.