A comparison of machine learning methods for automated gamma-ray spectroscopy

Kamuda, Mark; Zhao, Jifu; Huff, Kathryn

doi:10.1016/j.nima.2018.10.063

Title: A comparison of machine learning methods for automated gamma-ray spectroscopy

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Abstract

Pattern recognition algorithms such as artificial neural networks (NNs) and convolution neural networks (CNNs) are prime candidates to perform automated gamma-ray spectroscopy. The way these models train and operate mimic how trained spectroscopists identify spectra. These models have shown promise in identifying gamma-ray spectra with large calibration drift and unknown background radiation fields. In this work, two algorithms for mixtures of radioisotopes based on NN and CNN are presented and evaluated.

Authors:

Kamuda, Mark ^[1]; Zhao, Jifu ^[1]; Huff, Kathryn ^[1]

Univ. of Illinois, Urbana-Champaign, IL (United States)

Publication Date:: 2018-10-01

Research Org.:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1524430

Alternate Identifier(s):: OSTI ID: 1636277

Grant/Contract Number:: NA0002534

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: 954; Journal ID: ISSN 0168-9002

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Automated Isotope Identification; Neural Networks; Gamma-ray Spectroscopy

Citation Formats


                    Kamuda, Mark, Zhao, Jifu, and Huff, Kathryn. A comparison of machine learning methods for automated gamma-ray spectroscopy.  United States: N. p., 2018. 
Web.  doi:10.1016/j.nima.2018.10.063.

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                    Kamuda, Mark, Zhao, Jifu, & Huff, Kathryn. A comparison of machine learning methods for automated gamma-ray spectroscopy.  United States.  https://doi.org/10.1016/j.nima.2018.10.063

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                    Kamuda, Mark, Zhao, Jifu, and Huff, Kathryn. Mon .  
"A comparison of machine learning methods for automated gamma-ray spectroscopy".  United States.  https://doi.org/10.1016/j.nima.2018.10.063.  https://www.osti.gov/servlets/purl/1524430.

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@article{osti_1524430,

  title        = {A comparison of machine learning methods for automated gamma-ray spectroscopy},

  author       = {Kamuda, Mark and Zhao, Jifu and Huff, Kathryn},

  abstractNote = {Pattern recognition algorithms such as artificial neural networks (NNs) and convolution neural networks (CNNs) are prime candidates to perform automated gamma-ray spectroscopy. The way these models train and operate mimic how trained spectroscopists identify spectra. These models have shown promise in identifying gamma-ray spectra with large calibration drift and unknown background radiation fields. In this work, two algorithms for mixtures of radioisotopes based on NN and CNN are presented and evaluated.},

  doi          = {10.1016/j.nima.2018.10.063},

  journal      = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},

  number       = ,

  volume       = 954,

  place        = {United States},

  year         = {2018},

  month        = {10}

}

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