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<p>The work of the present study is to use machine learning methods, particularly the k-Means clustering algorithm, to classify and differentiate the <sup>131m</sup>Xe and <sup>133</sup>Xe radioactive spectra. Several methods of data organizing are implemented to investigate the algorithm’s precision in identifying and grouping distinct energy spectra. Overall, this investigation shows machine learning can be used to categorize distinct energy spectra and provide accurate isotope identification.</p>

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Evaluation of machine learning algorithms for nuclear monitoring applications

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Joshua Pace,
  • Steven Biegalski

摘要

The work of the present study is to use machine learning methods, particularly the k-Means clustering algorithm, to classify and differentiate the 131mXe and 133Xe radioactive spectra. Several methods of data organizing are implemented to investigate the algorithm’s precision in identifying and grouping distinct energy spectra. Overall, this investigation shows machine learning can be used to categorize distinct energy spectra and provide accurate isotope identification.