Automated isotope identification algorithm using artificial neural networks

Kamuda, Mark; Stinnett, Jacob; Sullivan, Clair

doi:10.1109/TNS.2017.2693152

Title: Automated isotope identification algorithm using artificial neural networks

Abstract

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.

Authors:

^[1]; Stinnett, Jacob ^[1];

^[1]

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

Publication Date:: Wed Apr 12 00:00:00 EDT 2017

Research Org.:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)

Contributing Org.:: Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, Il, 61801 USA.

OSTI Identifier:: 1367506

Grant/Contract Number:: NA0002534

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Nuclear Science

Additional Journal Information:: Journal Volume: 64; Journal Issue: 7; Journal ID: ISSN 0018-9499

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; artificial neural network; automated isotope identification; gamma-ray spectroscopy; machine learning

Citation Formats


                    Kamuda, Mark, Stinnett, Jacob, and Sullivan, Clair. Automated isotope identification algorithm using artificial neural networks.  United States: N. p., 2017. 
Web.  doi:10.1109/TNS.2017.2693152.

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                    Kamuda, Mark, Stinnett, Jacob, & Sullivan, Clair. Automated isotope identification algorithm using artificial neural networks.  United States.  https://doi.org/10.1109/TNS.2017.2693152

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                    Kamuda, Mark, Stinnett, Jacob, and Sullivan, Clair. Wed .  
"Automated isotope identification algorithm using artificial neural networks".  United States.  https://doi.org/10.1109/TNS.2017.2693152.  https://www.osti.gov/servlets/purl/1367506.

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@article{osti_1367506,

  title        = {Automated isotope identification algorithm using artificial neural networks},

  author       = {Kamuda, Mark and Stinnett, Jacob and Sullivan, Clair},

  abstractNote = {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.},

  doi          = {10.1109/TNS.2017.2693152},

  journal      = {IEEE Transactions on Nuclear Science},

  number       = 7,

  volume       = 64,

  place        = {United States},

  year         = {Wed Apr 12 00:00:00 EDT 2017},

  month        = {Wed Apr 12 00:00:00 EDT 2017}

}

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Title: Automated isotope identification algorithm using artificial neural networks

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