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Automated integration gate selection for Gaussian mixture model pulse shape discrimination

Kaplan, Alan D.·Blair, Brenton·Glenn, Andrew·Wurtz, Ron
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Abstract

Pulse shapes differ between neutron and gamma particles when measured with detector devices employing pulse shape discriminating (PSD) scintillators. Digitized waveforms can be used in detection systems to perform pulse shape discrimination for this application. Prior Gaussian Mixture Model (GMM) methods require access to the pulse full-waveform. Reducing the waveform to a smaller set of combined samples reduces computational cost while affecting PSD performance. In this work, we develop a method for selecting the best performing combination of integration gates, or contiguous summed segments of the digitized pulse for PSD. The method uses a discrimination score based on the GMM PSD approach. Furthermore, the final selection is performed using Bayesian Optimization. PSD detection results are compared with varying numbers of selected gates on time-of-flight (TOF) data. This method can be used to fully automate the selection of gates in an unsupervised (without ground truth labels) setting.

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42 ENGINEERING73 NUCLEAR PHYSICS AND RADIATION PHYSICSBayesian optimizationGate selectionGaussian mixture modelNeutron detectionPileupPulse shape discrimination

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Kaplan, Alan D., Blair, Brenton, Glenn, Andrew, Wurtz, Ron. 2023-07-01. Automated integration gate selection for Gaussian mixture model pulse shape discrimination. https://doi.org/10.1016/j.nima.2023.168486

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