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

Mark Kamuda, Jifu Zhao, Kathryn Huff

Research output: Contribution to journalReview article

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

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Instrumentation

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