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 ne...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 γ 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 shortterm 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 γ spectrum.展开更多
因人工放射性核素的航空γ能谱仪实物刻度模型匮乏,导致难以依据航空γ能谱准确反演地面人工放射性核素的含量。本文基于窄束γ射线指数衰变规律与微积分的思想建立了任意形状的γ辐射源上空航空γ能谱仪无源效率刻度的数值计算模型。...因人工放射性核素的航空γ能谱仪实物刻度模型匮乏,导致难以依据航空γ能谱准确反演地面人工放射性核素的含量。本文基于窄束γ射线指数衰变规律与微积分的思想建立了任意形状的γ辐射源上空航空γ能谱仪无源效率刻度的数值计算模型。通过低空探测实验、高空变化趋势分析、5-100 m高空探测实验证明该模型适用于任意位置点源航空γ能谱仪全能峰探测效率数值计算。同时计算发现在低空探测时不同γ辐射的面源与体源的航空γ能谱仪全能峰探测效率与MCNP5模拟值的相对偏差在±1.5%以内,且含1 460.83 ke V或2 614.533 ke Vγ射线的无限大体源90-150 m探测高空计算结果与石家庄动态带上的实验值相对偏差为8.33%-15.82%。上述实验充分证实该无源效率刻度计算模型适用于航空γ能谱探测实践,为利用航空γ能谱仪寻找丢失放射源及核事故应急监测提供技术支持。展开更多
基金supported by the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province(Nos.2024NSFSC0422,23NSFSCC0116)Nuclear Energy Development Project(No.[2021]-88).
文摘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 γ 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 shortterm 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 γ spectrum.
文摘因人工放射性核素的航空γ能谱仪实物刻度模型匮乏,导致难以依据航空γ能谱准确反演地面人工放射性核素的含量。本文基于窄束γ射线指数衰变规律与微积分的思想建立了任意形状的γ辐射源上空航空γ能谱仪无源效率刻度的数值计算模型。通过低空探测实验、高空变化趋势分析、5-100 m高空探测实验证明该模型适用于任意位置点源航空γ能谱仪全能峰探测效率数值计算。同时计算发现在低空探测时不同γ辐射的面源与体源的航空γ能谱仪全能峰探测效率与MCNP5模拟值的相对偏差在±1.5%以内,且含1 460.83 ke V或2 614.533 ke Vγ射线的无限大体源90-150 m探测高空计算结果与石家庄动态带上的实验值相对偏差为8.33%-15.82%。上述实验充分证实该无源效率刻度计算模型适用于航空γ能谱探测实践,为利用航空γ能谱仪寻找丢失放射源及核事故应急监测提供技术支持。
