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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.

Authors:
 [1];  [1];  [1]
  1. Univ. of Illinois, Urbana-Champaign, IL (United States)
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1524430
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 Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment; 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.
Kamuda, Mark, Zhao, Jifu, & Huff, Kathryn. A comparison of machine learning methods for automated gamma-ray spectroscopy. United States. doi:10.1016/j.nima.2018.10.063.
Kamuda, Mark, Zhao, Jifu, and Huff, Kathryn. Mon . "A comparison of machine learning methods for automated gamma-ray spectroscopy". United States. doi:10.1016/j.nima.2018.10.063. https://www.osti.gov/servlets/purl/1524430.
@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 = ,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
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