An automated isotope identification and quantification algorithm for isotope mixtures in low-resolution gamma-ray spectra

Journal Article · Fri Jun 15 00:00:00 EDT 2018 · Radiation Physics and Chemistry (1993)

DOI:https://doi.org/10.1016/j.radphyschem.2018.06.017· OSTI ID:1798655

Kamuda, Mark ^[1]; Sullivan, Clair J. ^[1]

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1. Univ. of Illinois at Urbana-Champaign, IL (United States)

There is a need to develop an automated isotope identification and quantification algorithm that can perform well using low-resolution gamma-ray detectors. The algorithm should be able to perform well on spectra that contain a mixture of many isotopes as well as in cases where spectral features are difficult to analyze. Due to the low resolution of these detectors, spectra of isotope mixtures becomes complicated to identify when features overlap. Previous research applying machine learning algorithms to isotope identification has been promising. Further work is needed to demonstrate the limits of machine learning algorithms when applied to identifying mixtures of isotopes and spectra where features are not obvious. Because machine learning algorithms use abstract features of the spectrum, such as the shape of overlapping peaks and Compton continuum, they are a natural choice for analyzing isotope mixtures. In this work, an artificial neural network (ANN) has be trained to calculate the relative activities of 29 isotopes in a spectrum. The ANN is trained with simulated gamma-ray spectra, allowing custom datasets to be generated for specific identification tasks. The algorithms performance on simulated spectra without apparent features and on simulated isotope mixtures are both analyzed.

• Research Organization: Univ. of Michigan, Ann Arbor, MI (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); Defense Threat Reduction Agency (DTRA)
• Grant/Contract Number: NA0002534; 10.13039/100000774
• OSTI ID: 1798655
• Alternate ID(s): OSTI ID: 1693977
• Journal Information: Radiation Physics and Chemistry (1993), Vol. 155; ISSN 0969-806X
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY
Automated Isotope Identification
Neural Networks
Gamma-ray Spectroscopy