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

Journal Article · 01 October 2018 · Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

DOI: https://doi.org/10.1016/j.nima.2018.10.063 · OSTI ID:1524430

Kamuda, Mark ^[1]; Zhao, Jifu ^[2]; Huff, Kathryn ^[2]

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1. Univ. of Illinois, Urbana-Champaign, IL (United States); University of Michigan
2. Univ. of Illinois, Urbana-Champaign, IL (United States)

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.

• Research Organization: Univ. of Michigan, Ann Arbor, MI (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0002534
• OSTI ID: 1524430
• Journal Information: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, Journal Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment Vol. 954; ISSN 0168-9002
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English