Determination of Gamma point source efficiency based on a back-propagation neural network

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, 11π/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.