K-Nearest Neighbors regression for the discrimination of gamma rays and neutrons in organic scintillators

Abstract

Certain organic scintillators, such as EJ-299 and stilbene, have the ability to distinguish gamma rays and neutrons through the process of pulse shape discrimination (PSD). This work introduces the novel application of a K-Nearest Neighbor (KNN) regressor to improve PSD performance, and applies it to six combinations of photosensors and organic scintillators, with a focus on silicon photomultiplier-based detectors. The method allows for direct comparison against conventional PSD methods by way of a typical PSD Figure of Merit (FOM), while avoiding the uncertainties associated with classification-based machine learning algorithms to achieve an assessment free from predetermined label bias. Tests were conducted to validate the proposed PSD regression method, and parameters such as input features and the number of nearest neighbors used were investigated to maximize the method’s performance. FOM values over a light output range of 200–700 keVee are shown, with improvements ranging from approximately 60% to over 200% when compared to conventional PSD methods. Finally, studies were performed to determine the lower bound of light output at which gamma ray and neutron groupings are still statistically separable. Results indicate that this light output bound can be lowered across all six tested detector-assembly combinations with the proposed KNN regression method when compared to the conventional PSD methods.