Contextual Isotope Ranking Criteria for Peak Identification in Gamma Spectroscopy Using a Large Database

Jun 22, 2022
12 pages
Published in:
  • IEEE Trans.Nucl.Sci. 69 (2022) 5, 1002-1013
e-Print:
DOI:

Citations per year

0 Citations
Abstract: (arXiv)
Isotope identification is a recurrent problem in gamma spectroscopy with high purity germanium detectors. In this work, new strategies are introduced to facilitate this type of analysis. Five criteria are used to identify the parent isotopes making a query on a large database of gamma-lines from a multitude of isotopes producing an output list whose entries are sorted so that the gamma-lines with the highest chance of being present in a sample are placed at the top. A metric to evaluate the performance of the different criteria is introduced and used to compare them. Two of the criteria are found to be superior than the others: one based on fuzzy logic, and another that makes use of the gamma relative emission probabilities. A program called histoGe implements these criteria using a SQLite database containing the gamma-lines of isotopes which was parsed from WWW Table of Radioactive Isotopes. histoGe is Free Software and is provided along with the database so they can be used to analyze spectra obtained with generic gamma-ray detectors.
Note:
  • 12 pages, 7 tables, 7 figures
  • [1]
    Practical gamma-ray spectroscopy. John
    • G. Gilmore
  • [2]

    Statistical methods applied to gamma-ray spectroscopy algorithms in nuclear security missions

    • D.K. Fagan
      ,
    • S.M. Robinson
      ,
    • R.C. Runkle
      • Appl.Radiat.Isot. 70 (2012) 2428-2439
  • [3]

    SPECTRW: a software package for nuclear and atomic spectroscopy

    • C. Kalfas
      ,
    • M. Axiotis
      ,
    • C. Tsabaris
      • Nucl.Instrum.Meth.A 830 (2016) 265-274
  • [4]

    HyperLab: A new concept in gamma-ray spectrum analysis

    • A. Simonits
      ,
    • J. Östör
      ,
    • S. Kálvin
      ,
    • B. Fazekas
      • J.Radioanal.Nucl.Chem. 257 (2003) 589-595
  • [5]

    GammaLab: a suite of programs for k 0-NAA and gammaray spectrum analysis

    • M. Wasim
      • J.Radioanal.Nucl.Chem. 285 (2010) 337-342
  • [6]

    The 2002 IAEA intercomparison of software for low-level γ-ray spectrometry

    • D. Arnold
      ,
    • M. Blaauw
      ,
    • S. Fazinic
      ,
    • V.P. Kolotov
  • [7]

    An intercomparison of software for processing Ge γ-ray spectra

    • S.P. Nielsen
      ,
    • S.E. Pálsson
  • [8]

    The 1995 IAEA intercomparison of γray spectrum analysis software

    • M. Blaauw
      ,
    • V.O. Fernandez
      ,
    • P. Van Espen
      ,
    • G. Bernasconi
      ,
    • R.C. Noy
    et al.
      • Nucl.Instrum.Meth.A 387 (1997) 416-432
  • [11]

    NUCDATA: a useful database for NAA lab

    • M. Wasim
      ,
    • J. Zaidi
  • [12]

    WWW Table of Radioactive Isotopes

    • R.F.S.Y.F. Chu
      ,
    • L.P. Ekström
  • [13]

    Live chart of nuclides

  • [14]

    National Nuclear Data Center

  • [15]

    Software ASPRO-NUC: gamma-ray spectrometry, routine NAA, isotope identification and data management

    • V. Kolotov
      ,
    • V. Atrashkevich
      • J.Radioanal.Nucl.Chem. 193 (1995) 195-206
  • [16]

    Interspec version 11.2.3

  • [18]

    Evaluation of key detector parameters for isotope identification

    • C.J. Sullivan
      ,
    • S. Garner
      ,
    • M. Lombardi
      ,
    • K. Butterfield
      ,
    • M. SmithNelson
  • [18]
    Nuclear Science Symposium Conference Record, vol. 2
    • C.J. Sullivan
      ,
    • S. Garner
      ,
    • M. Lombardi
      ,
    • K. Butterfield
      ,
    • M. SmithNelson
  • [19]

    Analysis of complex gamma-ray spectra using particle swarm optimization

    • H. Shahabinejad
      ,
    • N. Vosoughi
      • Nucl.Instrum.Meth.A 911 (2018) 123-130
  • [20]

    A gamma-ray identification algorithm based on Fisher linear discriminant analysis

    • D. Boardman
      ,
    • A. Flynn
      • IEEE Trans.Nucl.Sci. 60 (2012) 270-277
  • [21]

    Validation of a Bayesian-based isotope identification algorithm

    • C.J. Sullivan
      ,
    • J. Stinnett
      • Nucl.Instrum.Meth.A 784 (2015) 298-305
  • [22]

    Quantification and uncertainty analysis of low-resolution gamma-ray spectrometry using bayesian inference

    • J. Kim
      ,
    • K.T. Lim
      ,
    • J. Kim
      ,
    • Y. Kim
      ,
    • H. Kim
    et al.
      • Nucl.Instrum.Meth.A 953 (2020) 163144
  • [23]

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

    • M. Kamuda
      ,
    • C.J. Sullivan
      • Radiat.Phys.Chem. 155 (2019) 281-286
  • [24]

    An RBF neural network approach in radionuclide identification of unknown sources utilizing γ-ray spectra

    • P.-L. Lagari