Nowadays, there has been a growing trend in the fields of high-energy physics (HEP) in its both parts experimental and phenomenological studies, to incorporate machine learning (ML) and its specialized branch, deep learning (DL). This review paper provides a thorough illustration of these applications using different DL approaches. The first part of the paper examines the basics of various particle physics types and sets up guidelines for assessing particle physics alongside the available learning models. Next, a detailed classification is provided for representing the jet images that are reconstructed in high energy collisions mainly with proton-proton collisions at well defined beam energies, covering various datasets, preprocessing techniques, and feature extraction and selection methods. The presented techniques can be applied to future hadron-hadron colliders (HLC) such as high luminosity LHC (HL-HLC) and future circular collider-hadron-hadron (FCC-hh). Next, the authors explore a number of AI models analysis designed specifically for images in HEP. We additionally undertake a closer look at the classification associated with images in hadron collisions, with an emphasis on Jets. In this review, we look into various state-of-the-art (SOTA) techniques in ML and DL, examining their implications for HEP demands. More precisely, this discussion tackles various applications in extensive detail, such as Jet tagging, Jet tracking, particle classification, and more. The review concludes with an analysis of the current state of HEP, using DL methodologies. It covers the challenges and potential areas for future research that will be illustrated for each application.

翻译：当前，在高能物理（HEP）的实验研究与唯象研究领域，采用机器学习（ML）及其分支深度学习（DL）的趋势日益显著。本文系统综述了基于不同DL方法的相关应用。论文第一部分梳理了各类粒子物理基础概念，并建立了结合现有学习模型评估粒子物理问题的指导原则。随后，对高能碰撞（主要是在确定束流能量下的质子-质子碰撞）中重建的喷注图像表示方法进行了细致分类，涵盖多种数据集、预处理技术以及特征提取与选择方法。所述技术可应用于未来强子-强子对撞机（HLC），如高亮度大型强子对撞机（HL-HLC）和未来环形对撞机-强子强子型（FCC-hh）。接下来，作者深入探讨了专为HEP图像设计的多种人工智能模型分析方法，并重点聚焦于强子碰撞中的喷注图像分类问题。本综述详细考察了ML与DL领域多种前沿技术，剖析其应对HEP需求的适用性。具体而言，文中详尽讨论了喷注标记、喷注追踪、粒子分类等多类应用场景。最后，本文基于DL方法论分析了HEP领域现状，针对每类应用阐述了当前面临的挑战及未来潜在研究方向。