Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Application of peak based-Bayesian statistical method for isotope identification and categorization of depleted, natural and low enriched uranium measured by LaBr3:Ce scintillation detector

  • Haluk Yucel (Ankara University, Institute of Nuclear Sciences, Besevler 10.Yil Campus) ;
  • Selin Saatci Tuzuner (Ankara University, Institute of Nuclear Sciences, Besevler 10.Yil Campus) ;
  • Charles Massey (International Atomic Energy Agency (IAEA), Department of Nuclear Safety and Security, Division of Nuclear Security Detection Science and Technology, Nuclear Security of Materials Outside of Regulatory Control Section(MORC), Vienna International Centre(VIC))
  • Received : 2022.10.14
  • Accepted : 2023.07.05
  • Published : 2023.10.25
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Abstract

Todays, medium energy resolution detectors are preferably used in radioisotope identification devices(RID) in nuclear and radioactive material categorization. However, there is still a need to develop or enhance « automated identifiers » for the useful RID algorithms. To decide whether any material is SNM or NORM, a key parameter is the better energy resolution of the detector. Although masking, shielding and gain shift/stabilization and other affecting parameters on site are also important for successful operations, the suitability of the RID algorithm is also a critical point to enhance the identification reliability while extracting the features from the spectral analysis. In this study, a RID algorithm based on Bayesian statistical method has been modified for medium energy resolution detectors and applied to the uranium gamma-ray spectra taken by a LaBr3:Ce detector. The present Bayesian RID algorithm covers up to 2000 keV energy range. It uses the peak centroids, the peak areas from the measured gamma-ray spectra. The extraction features are derived from the peak-based Bayesian classifiers to estimate a posterior probability for each isotope in the ANSI library. The program operations were tested under a MATLAB platform. The present peak based Bayesian RID algorithm was validated by using single isotopes(241Am, 57Co, 137Cs, 54Mn, 60Co), and then applied to five standard nuclear materials(0.32-4.51% at.235U), as well as natural U- and Th-ores. The ID performance of the RID algorithm was quantified in terms of F-score for each isotope. The posterior probability is calculated to be 54.5-74.4% for 238U and 4.7-10.5% for 235U in EC-NRM171 uranium materials. For the case of the more complex gamma-ray spectra from CRMs, the total scoring (ST) method was preferred for its ID performance evaluation. It was shown that the present peak based Bayesian RID algorithm can be applied to identify 235U and 238U isotopes in LEU or natural U-Th samples if a medium energy resolution detector is was in the measurements.

Keywords

Acknowledgement

This work is fully supported by International Atomic Energy Agency (IAEA) J02012 coded Coordinated Research Project (2017-2022) on Advancing Radiation Detection Equipment for Detecting Nuclear and Other Radioactive Material out of Regulatory Control (MORC), which is under contract Nr.20908, entitled "Method Development for High, Medium and Low Resolution Detector Based Gamma Spectroscopic Determination of 235U Isotopic Abundance in Nuclear Material Detection and Characterization".

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