Radionuclide identification method for NaI low-count gamma-ray spectra using artificial neural network
| Autor: | Ye Chen, Jinglun Li, Xianyun Ai, Sheng Qi, Kun Zhang, Hui Zhao, Haijun Fan, Shanqiang Wang |
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| Rok vydání: | 2022 |
| Předmět: |
Artificial neural network
Physics Radionuclide Gamma-ray spectrum Branching fraction TK9001-9401 Detector Scintillator Computational physics Low-count spectrum Signal-to-noise ratio Nuclear Energy and Engineering Radionuclide identification Electromagnetic shielding Nuclear engineering. Atomic power Nuclide |
| Zdroj: | Nuclear Engineering and Technology, Vol 54, Iss 1, Pp 269-274 (2022) |
| ISSN: | 1738-5733 |
| Popis: | 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 N c = 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 N c >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. |
| Databáze: | OpenAIRE |
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