首页|A nuclide identification method of γ spectrum and model building based on the transformer
A nuclide identification method of γ spectrum and model building based on the transformer
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In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent neural networks,etc.,have been limited in research on y spectrum analysis because of their inherent mathematical mechanisms.It is difficult to make progress in terms of training data requirements and prediction accuracy.In contrast to traditional network paradigms,network models based on the transformer structure have the characteristics of parallel computing,position encoding,and deep stacking,which have enabled good performance in natural language processing tasks in recent years.Therefore,in this paper,a transformer-based neural network(TBNN)model is proposed to achieve nuclide identification for the first time.First,the Geant4 program was used to generate the basic single-nuclide energy spectrum through Monte Carlo simulations.A multi-nuclide energy spectrum database was established for neural network training using random matrices of γ-ray energy,activity,and noise.Based on the encoder-decoder structure,a network topology based on the transformer was built,transforming the 1024-channel energy spectrum data into a 32 × 32 energy spectrum sequence as the model input.Through experiments and adjustments of model parameters,including the learning rate of the TBNN model,number of attention heads,and number of network stacking layers,the overall recognition rate reached 98.7％.Additionally,this database was used for training AI models such as back-propagation networks,convolutional neural networks,residual networks,and long short-term memory neural networks,with overall recognition rates of 92.8％,95.3％,96.3％,and 96.6％,respectively.This indicates that the TBNN model exhibited better nuclide identification among these AI models,providing an important reference and theoretical basis for the practical application of transformers in the qualitative and quantitative analysis of the y spectrum.
Nuclide identificationNeural networkTransformer
Fei Li、Chu-Yang Luo、Ying-Zi Wen、Sheng Lv、Feng Cheng、Guo-Qiang Zeng、Jian-Feng Jiang、Bing-Hai Li
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College of Nuclear Technology and Automation Engineering,Chengdu University of Technology,Chengdu 610000,China
Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province,Chengdu 610000,China
Department of Physics,Universitat Politècnica de Catalunya,Barcelona 08028,Spain
Airborne Survey and Remote Sensing Center of Nuclear Industry,Shijiazhuang 050000,China
Hebei Key Laboratory of Airborne Detection and Remote Sensing Technology,Shijiazhuang 050000,China
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NSTL
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