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Title: Automated integration gate selection for Gaussian mixture model pulse shape discrimination

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.

Authors:
 [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
+ Show Author Affiliations
  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
OSTI Identifier:
2001194
Report Number(s):
LLNL-JRNL-842188
Journal ID: ISSN 0168-9002; 1064172; TRN: US2405609
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Additional Journal Information:
Journal Volume: 1055; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; 42 ENGINEERING; Pulse shape discrimination; Gaussian mixture model; Bayesian optimization; Gate selection; Pileup; Neutron detection

Citation Formats

Kaplan, Alan D., Blair, Brenton, Glenn, Andrew, and Wurtz, Ron. Automated integration gate selection for Gaussian mixture model pulse shape discrimination. United States: N. p., 2023. Web. doi:10.1016/j.nima.2023.168486.
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Kaplan, Alan D., Blair, Brenton, Glenn, Andrew, & Wurtz, Ron. Automated integration gate selection for Gaussian mixture model pulse shape discrimination. United States. https://doi.org/10.1016/j.nima.2023.168486
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Kaplan, Alan D., Blair, Brenton, Glenn, Andrew, and Wurtz, Ron. Sat . "Automated integration gate selection for Gaussian mixture model pulse shape discrimination". United States. https://doi.org/10.1016/j.nima.2023.168486.
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@article{osti_2001194,
title = {Automated integration gate selection for Gaussian mixture model pulse shape discrimination},
author = {Kaplan, Alan D. and Blair, Brenton and Glenn, Andrew and Wurtz, Ron},
abstractNote = {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.},
doi = {10.1016/j.nima.2023.168486},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = ,
volume = 1055,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2023},
month = {Sat Jul 01 00:00:00 EDT 2023}
}
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Journal Article:
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https://doi.org/10.1016/j.nima.2023.168486
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