Pulse discrimination with a Gaussian mixture model on an FPGA
Journal Article
·
· Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Computational Engineering Division
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Physics Division
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.
- 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
- OSTI ID:
- 1474377
- Alternate ID(s):
- OSTI ID: 1582956
- Report Number(s):
- LLNL-JRNL-746701; 931194
- Journal Information:
- Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 900, Issue C; ISSN 0168-9002
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 17 works
Citation information provided by
Web of Science
Web of Science
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