Automated isotope identification algorithm using artificial neural networks

Journal Article · 12 April 2017 · IEEE Transactions on Nuclear Science

DOI: https://doi.org/10.1109/TNS.2017.2693152 · OSTI ID:1367506

Kamuda, Mark  ^[1]; Stinnett, Jacob ^[1]; Sullivan, Clair  ^[1]

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

There is a need to develop an algorithm that can determine the relative activities of radio-isotopes in a large dataset of low-resolution gamma-ray spectra that contains a mixture of many radio-isotopes. Low-resolution gamma-ray spectra that contain mixtures of radio-isotopes often exhibit feature over-lap, requiring algorithms that can analyze these features when overlap occurs. While machine learning and pattern recognition algorithms have shown promise for the problem of radio-isotope identification, their ability to identify and quantify mixtures of radio-isotopes has not been studied. 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 radio-isotope mixtures. An artificial neural network (ANN) has be trained to calculate the relative activities of 32 radio-isotopes in a spectrum. Furthermore, the ANN is trained with simulated gamma-ray spectra, allowing easy expansion of the library of target radio-isotopes. In this paper we present our initial algorithms based on an ANN and evaluate them against a series measured and simulated spectra.

• Research Organization: Univ. of Michigan, Ann Arbor, MI (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
• Contributing Organization: Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, Il, 61801 USA.
• Grant/Contract Number: NA0002534
• OSTI ID: 1367506
• Journal Information: IEEE Transactions on Nuclear Science, Vol. 64, Issue 7; ISSN 0018-9499
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English