Pulse pileup rejection methods using a two-component Gaussian Mixture Model for fast neutron detection with pulse shape discriminating scintillator

Abstract

Pulse shape discriminating scintillator materials in many cases allow the user to identify two basic kinds of pulses arising from two kinds of particles: neutrons and gammas, respectively. An uncomplicated solution for building a classifier consists of a two-component mixture model learned from mixtures of pulses from neutrons and gammas at a range of energies. Depending on the conditions of data gathered to be classified, multiple classes of events besides neutrons and gammas may occur, most notably pileup events. All these kinds of events that are neither neutron nor gamma are anomalous and, in cases where the class of the particle is in doubt, it is preferable to remove them from the analysis. This study compares the performance of two analytical methods for using the scores from the two-component model to identify anomalous events and in particular to remove pileup events. This study further benchmarks the analytical methods against supervised machine learning methods. This study also presents a means of assessing performance of pileup removal using ROC curves and precision–recall curves. A specific outcome of this study is to propose a novel anomaly score, denoted by G, from an unsupervised two-component model that is conveniently distributed on the interval [−1,1].