ResNet and CycleGAN for pulse shape discrimination of He-4 detector pulses: Recovering pulses conventional algorithms fail to label unanimously.

Pulse shape discrimination (PSD) capable detectors, such as He-4, that respond to neutron and gamma-ray interactions have a threshold deposited energy value below which n/γ discrimination vanishes when using conventional PSD algorithms. Recent attempts in applying supervised learning based artificial neural networks for PSD use the pulses in the separated regions to train the networks so they can be used to classify another set of separated pulses. In doing so, pulses previously indistinguishable are not recovered for classification, which would have increased the number of neutron and gamma-ray pulses that could be used for further analysis. Assuming the reason why conventional PSD algorithms have unseparated regions is because the parameter space of the algorithms fail to capture the intrinsic (but subtle) distinguishing behavior of some of the neutron and gamma-ray pulses, a cycle-consistent generative adversarial network (CycleGAN) was trained to amplify those differences and extract well separated neutron and gamma-ray clusters. Results show that, once the network is trained with pulses from separated and unseparated regions, it was able to transform the pulses in the unseparated region to improve the PSD. Subsequent n/γ classification was performed using deep residual network (ResNet) that takes pulses with 512 data points as an input. Two different ResNets were explored - simple ResNet and modified ResNet which takes segmented pulse inputs in the first layer and the corresponding time axis values in the last hidden layer. The later approach enables the network to extract time correlated pulse features to enhance its ability to capture the pulse behaviors relevant for PSD. Although it achieves slightly lower accuracy, 99.41% versus 99.89%, based on simply counting the number of correct n/γ labels assigned, compared to the simple ResNet, the modified ResNets architecture was able to decreases the cross-entropy loss function by half, which implies that the correct n/γ labels assigned are less likely to be accidental. PSD parameter distributions based on n/γ classification by ResNet before and after transforming unseparated pulses using CycleGAN show that by enhancing the separation between neutrons and gamma-rays, the transformation helps improve the performance of classifier networks that are trained using labeled dataset. The enhancement of neutron and gamma-ray separation by the CycleGAN increased the PSD figure of merit (FOM) by up to 70% in some regions. The results show that, if a given detector achieves clear separation between neutron and gamma-ray pulses in any energy region, such neural network approaches can help lower the energy threshold for the separation and increasing the number of neutron and gamma-ray pulses that can be used for further analysis.