Unsupervised Machine Learning Approaches to Nuclear Particle Type Classification

Historically, nuclear science and radiation detection fields of research used Pulse Shape Discrimination (PSD) to label gamma-ray and neutron interactions. However, PSD’s effectiveness relies greatly on the existence of distinguishable differences in an interaction’s measured pulse shape. In the fields of machine learning and data analytics, clustering algorithms provide ways to group samples with similar features without the need for labels. Clustering gamma-ray and neutron interactions may mitigate PSD’s pitfalls, since clustering methods view the total waveform rather than just the area under the tail and the total area under the pulse. However, traditional clustering methods, such as the k-means clustering algorithm, suffer from poor performance on high dimensional data. This study explores unsupervised machine learning methods using Deep Neural Networks (DNN) to cluster gamma-ray and neutron interaction measurements collected with an organic scintillation detector, in order to perform binary labeling of gamma-rays and neutrons. Using various network architectures, this research demonstrates the effectiveness of using autoencoder-based neural networks to cluster gamma-ray and neutron interactions when compared to shallow clustering algorithms. The results reveal the effectiveness of autoencoders on high energy gamma-ray and neutron pulses with an energy deposit greater than 0.80 MeVee whilst greatly outperforming k-means comparatively in all cases.