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

Journal Article · · Statistical Analysis and Data Mining
DOI:https://doi.org/10.1002/sam.11432· OSTI ID:1604282
ORCiD logo [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Computational Engineering Division
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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.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
Grant/Contract Number:
AC52-07NA27344; AC52‐07NA27344
OSTI ID:
1604282
Alternate ID(s):
OSTI ID: 1544913
Report Number(s):
LLNL-JRNL-752600; 938753
Journal Information:
Statistical Analysis and Data Mining, Vol. 12, Issue 6; ISSN 1932-1864
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

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Figures / Tables (14)

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Related Subjects

97 MATHEMATICS AND COMPUTING
classification
clustering
mixture models
pulse shape discrimination