Image Classification in High-Energy Physics: A Comprehensive Survey of Applications to Jet Analysis

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.