摘要：

近年来, 深度学习在计算机视觉、自然语言处理等应用领域取得了巨大的成功, 致使高能物理研究者也开始关注深度学习技术, 并探索其在强子喷注标记任务中的应用。最初研究者将喷注数据转化成图像和序列数据, 采用卷积神经网络(CNN)和循环神经网络(RNN)对喷注进行标记, 但存在计算效率低和可解释性差的问题。为了解决这些问题, 研究者对网络结构进行了多方面的改进, 并在构建的多种喷注标记数据集上进行训练, 提升了模型分类的性能。本文对新型网络模型的重要模块进行深入分析综述, 包括基于集合表示喷注的方法、等变性神经网络的应用以及喷注基础模型的探索。同时, 对各种标记分类器进行了分析和比较, 评估各种网络结构的性能, 并对相关模型现状进行了分析与总结, 探讨了深度学习模型在喷注标记任务中的应用前景。

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

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.

Key words: Convolutional Neural Network (CNN), Transformer model, Jet Tagging, equivariant neural network, Foundation Model (FM)