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Title: Pulse discrimination with a Gaussian mixture model on an FPGA

Abstract

A Gaussian Mixture Model (GMM) based machine learning algorithm has been applied to the problem of gamma/neutron pulse shape discrimination (PSD). The algorithm has been successfully implemented on a standard PC as well as a field programmable gate array (FPGA). We describe the GMM classifier and its implementation on these two different types of hardware. We compare the performance of the algorithm on these two platforms against each other, along with other standard techniques applied in PSD. Our results show that the FPGA-based GMM classifier outperforms the standard PSD techniques in terms of classification accuracy at low particle energy and executes more quickly than its CPU-based counterpart.

Authors:
 [1];  [1];  [2];  [2];  [1];  [2]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Computational Engineering Division
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Physics 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 (NA-20)
OSTI Identifier:
1474377
Report Number(s):
LLNL-JRNL-746701
Journal ID: ISSN 0168-9002; 931194
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: 900; Journal Issue: C; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; 97 MATHEMATICS AND COMPUTING; Pulse shape discrimination; Gaussian mixture model; Neutron detection; Field-programmable gate array

Citation Formats

Simms, Lance M., Blair, Brenton, Ruz, Jaime, Wurtz, Ron, Kaplan, Alan D., and Glenn, Andrew. Pulse discrimination with a Gaussian mixture model on an FPGA. United States: N. p., 2018. Web. doi:10.1016/j.nima.2018.05.039.
Simms, Lance M., Blair, Brenton, Ruz, Jaime, Wurtz, Ron, Kaplan, Alan D., & Glenn, Andrew. Pulse discrimination with a Gaussian mixture model on an FPGA. United States. doi:10.1016/j.nima.2018.05.039.
Simms, Lance M., Blair, Brenton, Ruz, Jaime, Wurtz, Ron, Kaplan, Alan D., and Glenn, Andrew. Wed . "Pulse discrimination with a Gaussian mixture model on an FPGA". United States. doi:10.1016/j.nima.2018.05.039. https://www.osti.gov/servlets/purl/1474377.
@article{osti_1474377,
title = {Pulse discrimination with a Gaussian mixture model on an FPGA},
author = {Simms, Lance M. and Blair, Brenton and Ruz, Jaime and Wurtz, Ron and Kaplan, Alan D. and Glenn, Andrew},
abstractNote = {A Gaussian Mixture Model (GMM) based machine learning algorithm has been applied to the problem of gamma/neutron pulse shape discrimination (PSD). The algorithm has been successfully implemented on a standard PC as well as a field programmable gate array (FPGA). We describe the GMM classifier and its implementation on these two different types of hardware. We compare the performance of the algorithm on these two platforms against each other, along with other standard techniques applied in PSD. Our results show that the FPGA-based GMM classifier outperforms the standard PSD techniques in terms of classification accuracy at low particle energy and executes more quickly than its CPU-based counterpart.},
doi = {10.1016/j.nima.2018.05.039},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = C,
volume = 900,
place = {United States},
year = {2018},
month = {5}
}

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