计算机工程2026，Vol.52Issue(8)：101-113,13.DOI:10.19678/j.issn.1000-3428.0252689
高能物理喷注标记的深度学习模型综述
Survey of Deep Learning Models in High Energy Physics Jet Tagging
摘要
Abstract
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.关键词
卷积神经网络/Transformer模型/喷注标记/等变性神经网络/基础模型Key words
Convolutional Neural Network(CNN)/Transformer model/Jet Tagging/equivariant neural network/Foundation Model(FM)分类
信息技术与安全科学引用本文复制引用
高六龙,黄正坤,姜晓巍,孙功星,李佳枫..高能物理喷注标记的深度学习模型综述[J].计算机工程,2026,52(8):101-113,13.基金项目
国家自然科学基金(11775249). （11775249）
