Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Radionuclide identification method for NaI low-count gamma-ray spectra using artificial neural network

  • Qi, Sheng (State Key Laboratory of NBC Protection for Civilian) ;
  • Wang, Shanqiang (State Key Laboratory of NBC Protection for Civilian) ;
  • Chen, Ye (State Key Laboratory of NBC Protection for Civilian) ;
  • Zhang, Kun (State Key Laboratory of NBC Protection for Civilian) ;
  • Ai, Xianyun (State Key Laboratory of NBC Protection for Civilian) ;
  • Li, Jinglun (State Key Laboratory of NBC Protection for Civilian) ;
  • Fan, Haijun (State Key Laboratory of NBC Protection for Civilian) ;
  • Zhao, Hui (State Key Laboratory of NBC Protection for Civilian)
  • Received : 2020.11.03
  • Accepted : 2021.07.16
  • Published : 2022.01.25
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Abstract

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum Nc = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the Nc>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of 235U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

Keywords

Acknowledgement

This work was supported by National Natural Science Foundation of China (12075318); State Key Laboratory of NBC Protection for Civilian (SKLNBC2018-01, SKLNBC2018-05, SKLNBC2020-04).

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