A Vision Transformer Based Method for Pileup Detection in Pulse Shape Discrimination

Reliably detecting and accurately counting neutrons in the presence of a strong γ ray background is critical to nuclear security and safeguards applications. It is very challenging especially at high count rates where pileup events are more likely to happen. If not detected, pileup is likely to be misclassified. Traditional approaches to pulse shape discrimination (PSD) and pileup rejection heavily rely on the manually selected features in either time or frequency domains. Their performance is susceptible to noise and complex high count rate scenarios. In this paper, vision transformers (ViT) are trained for PSD and pileup detection based on the continuous wavelet transform of the pulses. The superiority of ViT over the traditional approaches and the other deep learning models in detecting the pileup pulses is demonstrated, even for the close pileup cases.