高能物理喷注标记的深度学习模型综述OA

In recent years,deep learning has achieved tremendous success in application fields such as computer vision and natural language processing.This has led researchers in high energy physics to also turn their attention to deep learning technologies and explore their application in hadronic Jet Tagging tasks.Initially,researchers converted Jet data into image and sequence data,and used Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)to tag Jets.However,these approaches suffered from problems such as low computational efficiency and poor interpretability.To address these issues,researchers have made improvements to network architectures from multiple perspectives and conducted training on various constructed Jet Tagging datasets,thereby enhancing the classification performance of the models.This paper provides an in-depth review of the key modules of novel network models,including methods for representing Jets based on sets,the application of equivariant neural networks,and the exploration of Jet foundation models.Meanwhile,this paper analyzes and compares various tagging classifiers,evaluates the performance of different network architectures,summarizes the current status of relevant models,and discusses the application prospects of deep learning models in Jet Tagging tasks.