期刊
出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.nima.2018.05.039
关键词
Pulse shape discrimination; Gaussian mixture model; Neutron detection; Field-programmable gate array
类别
资金
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- U.S. Department of Energy Office of Defense Nuclear Nonproliferation Research and Development
- agency of the United States government
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). Here 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.
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