A new radionuclide identification method for low-count energy spectra with multiple radionuclides.

Journal: Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
PMID: 35413589

Abstract

Radionuclide identification is to recognize the radionuclides in the environment by analyzing the energy spectrum. Rapid and accurate identification is important for nuclear security. Current radionuclide identification methods based on traditional peak search require background subtraction. As a result, they have difficulties to deal with complex situations in practical applications such as low-count energy spectrum and mixed nuclides. In this paper, we propose a new radionuclide identification method with a feature enhancer and a one-dimensional neural network. The training dataset in this method is from simulated data generated by Geant4. By preprocessing the input energy spectrum data through the feature enhancer and extracting the nonlinear information through the neural network, this approach performs well on experimental energy spectra even at low count. The method also shows a high recognition accuracy and little misjudgments when dealing with mixed radionuclides spectra. Due to its good performance in identifying mixed nuclides and low-count spectra, the method has been deployed in portable instrument for radionuclide identification in real-time measurement.

Authors

  • Chunmiao Li
    Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: lichm@ihep.ac.cn.
  • Shuangquan Liu
    The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, P. R. China.
  • Chao Wang
    College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Xiaopan Jiang
    Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; Jinan Laboratory of Applied Nuclear Science, Jinan, 250131, China.
  • Xiaoli Sun
    School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China.
  • Mohan Li
    College of Food Science Shenyang Agricultural University Shenyang China.
  • Long Wei
    Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Jinan Laboratory of Applied Nuclear Science, Jinan, 250131, China. Electronic address: weilong@ihep.ac.cn.

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