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Title: Gaussian mixture models as automated particle classifiers for fast neutron detectors

Abstract

Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold. As such, in this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Computational Engineering Division
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
OSTI Identifier:
1604282
Alternate Identifier(s):
OSTI ID: 1544913
Report Number(s):
LLNL-JRNL-752600
Journal ID: ISSN 1932-1864; 938753
Grant/Contract Number:  
AC52-07NA27344; AC52‐07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Volume: 12; Journal Issue: 6; Journal ID: ISSN 1932-1864
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; classification; clustering; mixture models; pulse shape discrimination

Citation Formats

Blair, Brenton, Chen, Cliff, Glenn, Andrew, Kaplan, Alan, Ruz, Jaime, Simms, Lance, and Wurtz, Ron. Gaussian mixture models as automated particle classifiers for fast neutron detectors. United States: N. p., 2019. Web. https://doi.org/10.1002/sam.11432.
Blair, Brenton, Chen, Cliff, Glenn, Andrew, Kaplan, Alan, Ruz, Jaime, Simms, Lance, & Wurtz, Ron. Gaussian mixture models as automated particle classifiers for fast neutron detectors. United States. https://doi.org/10.1002/sam.11432
Blair, Brenton, Chen, Cliff, Glenn, Andrew, Kaplan, Alan, Ruz, Jaime, Simms, Lance, and Wurtz, Ron. Thu . "Gaussian mixture models as automated particle classifiers for fast neutron detectors". United States. https://doi.org/10.1002/sam.11432. https://www.osti.gov/servlets/purl/1604282.
@article{osti_1604282,
title = {Gaussian mixture models as automated particle classifiers for fast neutron detectors},
author = {Blair, Brenton and Chen, Cliff and Glenn, Andrew and Kaplan, Alan and Ruz, Jaime and Simms, Lance and Wurtz, Ron},
abstractNote = {Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold. As such, in this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement.},
doi = {10.1002/sam.11432},
journal = {Statistical Analysis and Data Mining},
number = 6,
volume = 12,
place = {United States},
year = {2019},
month = {7}
}

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