Research on nuclide identification method based on convolutional recurrent neural network

Shan Yansong; Zhang Jiangmei; Liu Haolin; Zhang Caolin

doi:10.11884/HPLPB202638.250174

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Article Navigation > High Power Laser and Particle Beams > 2026 > Accepted Manuscript

Shan Yansong, Zhang Jiangmei, Liu Haolin, et al. Research on nuclide identification method based on convolutional recurrent neural network[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250174

Citation:

Shan Yansong, Zhang Jiangmei, Liu Haolin, et al. Research on nuclide identification method based on convolutional recurrent neural network[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250174

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Research on nuclide identification method based on convolutional recurrent neural network

doi: 10.11884/HPLPB202638.250174

1.
School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China
2.
Caea Innovation Center of Nuclear Environmental Safety Technology, Southwest University of Science and Technology, Mianyang 621010, China

Received Date: 2025-06-16
Accepted Date: 2025-11-08
Rev Recd Date: 2025-11-17

Available Online: 2025-11-26

Abstract

Abstract

Background
Accurate identification of radionuclides is the key to improving the level of radioactivity monitoring.
Purpose
To further enhance the performance of radionuclide identification, a method combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for radionuclide identification has been studied.
Methods
Gamma-ray spectra data of eight single and mixed radioactive nuclides were collected using a sodium iodide spectrometer, and a large number of gamma-ray spectral training data were generated by calculating the probability density of gamma photons at different energy levels and using random sampling methods, followed by normalization of the data. The CNN was then used to extract feature vectors from the input spectral data, and these extracted feature vectors were fed into the RNN for training, with the final radionuclide classification results being output by the activation function.
Results
To verify the accuracy of the CNN-RNN method in identifying radionuclides, a comparative analysis was conducted with the radionuclide identification method based on Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM), and the results showed that the LSTM spectral model achieved a recognition accuracy rate of over 97.5% for single nuclides and over 92.31% for mixed nuclides on the test set, while the CNN and CNN-RNN spectral models achieved a recognition accuracy rate of 100% for single nuclides and recognition rates of over 92.95% and 97.44% for mixed nuclides.
Conclusions
respectively, indicating that the CNN-RNN method performs better in gamma-ray spectral identification of radioactive nuclides, Compared with neural network models trained only on real - measured data, incorporating augmented data can improve the training efficiency and generalization ability of the models.
- convolutional neural network,
- recurrent neural network,
- nuclide identification,
- radioactive nuclides,
- gamma-ray spectra

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References(19)

References

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