| Summary: | BackgroundIn the measurement of radioactive waste drums in nuclear power plants, the traditional analytical method of γ energy spectrum has the problems of nuclide misjudgment and poor accuracy of peak area calculation.PurposeThis study aims to evaluate the performance of an analytical method for γ energy spectrum based on deep neural network.MethodsThe whole data of γ energy spectrum were taken by the deep neural network model as the analysis object, hence no need of the traditional methods such as spectral line smoothing and peak searching. First of all, combinations of five different γ sources placed in different positions in the 200 L steel drum from Qinshan nuclear power plant-phase I, filled three different media (air, water and sand) were experimental measured by using digital γ-ray spectrometer and high purity germanium (HPGe) detector. Then the γ energy spectra obtained by Monte Carlo simulation using the model of the same experimental measurement system were used as the data set of the neural network training. Finally, γ energy spectra obtained by experiments were compared with simulated for verification.ResultsThe trained deep neural network converges quickly, both the nuclides identification and peak area calculation are fast with the accuracy of 96.47%. Hardly misidentification of nuclides is caused for the complex energy spectrum of multi-nuclide mixture, and less than 10% identification error for the peak area calculation of a weak peak in the γ energy spectrum.ConclusionsThe analytical method based on deep neural network is suitable for the analysis for γ energy spectrum of radioactive waste drums, and the spectrum resolution accuracy is better than the traditional method.
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