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

Mark Kamuda, Jifu Zhao, Kathryn D Huff

Research output: Contribution to journalArticle

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.

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machine learning
Gamma rays
Learning systems
Spectroscopy
gamma rays
Neural networks
Convolution
convolution integrals
spectroscopy
background radiation
gamma ray spectra
Radioisotopes
pattern recognition
radiation distribution
Pattern recognition
Calibration
Radiation

Keywords

  • Automated isotope identification
  • Gamma-ray spectroscopy
  • Neural networks

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Instrumentation

Cite this

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title = "A comparison of machine learning methods for automated gamma-ray spectroscopy",
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.",
keywords = "Automated isotope identification, Gamma-ray spectroscopy, Neural networks",
author = "Mark Kamuda and Jifu Zhao and Huff, {Kathryn D}",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.nima.2018.10.063",
language = "English (US)",
journal = "Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment",
issn = "0168-9002",
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AU - Zhao, Jifu

AU - Huff, Kathryn D

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N2 - 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.

AB - 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.

KW - Automated isotope identification

KW - Gamma-ray spectroscopy

KW - Neural networks

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JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

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