Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2018, Vol. 29 ›› Issue (5): 61

• NUCLEAR ELECTRONICS AND INSTRUMENTATION •

Determination of Gamma point source efficiency based on a backpropagation neural network

Hong-Long Zheng 1,2 • Xian-Guo Tuo 2,3 • Shu-Ming Peng 1 • Rui Shi 2,3 • Huai-Liang Li 3 • Jing Lu 2,3 • Jin-Fu Li 3

1. 1 Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang 621900, China
2 College of Chemistry and Environmental Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
3 Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010, China
• Contact: Xian-Guo Tuo E-mail:myconnectionmail@126.com
• Supported by:

This work was supported by the National Natural Science Foundation of China (Nos. 41374130 and 41604154), Science and Technology Program of Sichuan, China (No. 2017GZ0359), Science and Technology Support Program of Sichuan, China (No. 2015JY0007), and Open Foundation for Artificial Intelligence Key Laboratory of Sichuan Province of China (No. 2016RYJ08).

Hong-Long Zheng, Xian-Guo Tuo, Shu-Ming Peng, Rui Shi, Huai-Liang Li, Jing Lu, Jin-Fu Li. Determination of Gamma point source efficiency based on a backpropagation neural network.Nuclear Science and Techniques, 2018, 29(5): 61
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Abstract:

Efficiency is an important factor in quantitative and qualitative analysis of radionuclides, and the gamma point source efficiency is related to the radial angle, detection distance, and gamma-ray energy. In this work, on the basis of a back-propagation (BP) neural network model, a method to determine the gamma point source efficiency is developed and validated. The efficiency of the point sources 137Cs and 60Co at discrete radial angles, detection distances, and gamma-ray energies is measured, and the BP neural network prediction model is constructed using MATLAB. The gamma point source efficiencies at different radial angles, detection distances, and gamma-ray energies are predicted quickly and accurately using this nonlinear prediction model. The results show that the maximum error between the predicted and experimental values is 3.732% at 661.661 keV, 11p/24, and 35 cm, and those under other conditions are less than 3%. The gamma point source efficiencies obtained using the BP neural network model are in good agreement with experimental data.