Radionuclide identification method based on a gamma-spectra template library simulated by radial basis function neural networks
DU Xiaochuang1,2, TU Hongbing1, LI Ke2, ZHANG Jie1, WANG Kang3, LIU Hemin1, LIANG Manchun2, WANG Xiangwei2
1. State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China; 2. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China; 3. Beijing Global Safety Technology Co., Ltd., Beijing 100094, China
Abstract：The traditional gamma ray spectrum analysis methods are usually computationally complex and time-consuming. This paper simulated 26 radionuclide spectra using Geant4 to develop a gamma-spectra template library by fitting the spectra with radial basis function neural networks. Unknown gamma ray spectra were then identified using a least-squares algorithm and a nonlinear programing algorithm to find the optimal combination of radionuclide spectra in the library that matched the unknown spectrum with ensemble learning used to integrate the results of the two algorithms for identification. A single spectrum and mixed spectra containing 2 or 3 kinds of radionuclides were generated to evaluate the method. The results show that this method can accurately identify the radionuclides in an efficient and effective way.
杜晓闯, 涂红兵, 黎岢, 张洁, 王康, 刘鹤敏, 梁漫春, 汪向伟. 基于径向基神经网络仿真γ能谱模板库的核素识别方法[J]. 清华大学学报（自然科学版）, 2021, 61(11): 1308-1315.
DU Xiaochuang, TU Hongbing, LI Ke, ZHANG Jie, WANG Kang, LIU Hemin, LIANG Manchun, WANG Xiangwei. Radionuclide identification method based on a gamma-spectra template library simulated by radial basis function neural networks. Journal of Tsinghua University(Science and Technology), 2021, 61(11): 1308-1315.
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