Application of a Novel Machine Learning Approach to SiPM-Based Neutron/Gamma Detection and Discrimination

Matthew Durbin, Marc A. Wonders, Azaree T. Lintereur, Marek Flaska

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Silicon photomultipliers (SiPMs) have become a common component of radiation detection systems and have shown promise in distinguishing neutrons and gammas when coupled with detectors sensitive to both particle types. This work investigates the use of a novel machine learning (ML) approach to aid in this discrimination. The proposed ML algorithm performs a regression on a conventionally calculated pulse shape parameter (PSP), producing a more representative PSP while allowing for direct comparison to the traditional pulse shape discrimination (PSD) technique. This work also investigates how the proposed ML-based PSD approach can benefit different scintillator and light-sensor combinations with varying levels of traditional PSD performance, especially those based on SiPMs. A preliminary implementation of the regression method with a Hamamatsu SiPM and stilbene scintillator combination on a data set of mixed gamma and neutron pulses from 252Cf resulted in an increase in the figure of merit of approximately 100% compared to the traditional PSD technique.

Original languageEnglish (US)
Title of host publication2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141640
DOIs
StatePublished - Oct 2019
Event2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019 - Manchester, United Kingdom
Duration: Oct 26 2019Nov 2 2019

Publication series

Name2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019

Conference

Conference2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
CountryUnited Kingdom
CityManchester
Period10/26/1911/2/19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Radiology Nuclear Medicine and imaging
  • Nuclear and High Energy Physics

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