TY - JOUR
T1 - A comparison of machine learning methods for automated gamma-ray spectroscopy
AU - Kamuda, Mark
AU - Zhao, Jifu
AU - Huff, Kathryn
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2020/2/21
Y1 - 2020/2/21
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
UR - http://www.scopus.com/inward/record.url?scp=85055748393&partnerID=8YFLogxK
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U2 - 10.1016/j.nima.2018.10.063
DO - 10.1016/j.nima.2018.10.063
M3 - Review article
AN - SCOPUS:85055748393
SN - 0168-9002
VL - 954
JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
M1 - 161385
ER -