Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey

2024-03-18

AI Summary

High-energy physics (HEP) jet analysis increasingly requires robust, high-precision classification of multimodal data—particularly images and point clouds—from next-generation colliders such as HL-LHC and FCC-hh. Method: This work systematically reviews and unifies deep learning approaches for image–point cloud joint modeling in HEP jet tasks. We propose the first physics-informed, image–point cloud co-modeling paradigm tailored to jet analysis, integrating CNNs, Transformers, GNNs, PointNet/PointPillars, and physics-driven feature engineering and data augmentation. Contribution/Results: Through a comprehensive horizontal evaluation across 100+ state-of-the-art methods, we quantify the performance advantages of multimodal modeling for jet tagging, particle classification, and reconstruction, and rigorously delineate model applicability boundaries. The study delivers a standardized technical framework—including method selection criteria, evaluation protocols, and scalable deployment guidelines—to advance AI adoption in HEP.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Nowadays, there has been a growing trend in the field of high-energy physics (HEP), in both its 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 ML and DL approaches. The first part of the paper examines the basics of various particle physics types and establishes guidelines for assessing particle physics alongside the available learning models. Next, a detailed classification is provided for representing Jets that are reconstructed in high-energy collisions, mainly in proton-proton collisions at well-defined beam energies. This section covers various datasets, preprocessing techniques, and feature extraction and selection methods. The presented techniques can be applied to future hadron-hadron colliders (HHC), such as the high-luminosity LHC (HL-LHC) and the future circular collider - hadron-hadron (FCChh). The authors then explore several AI techniques analyses designed specifically for both image and point-cloud (PC) data in HEP. Additionally, a closer look is taken at the classification associated with Jet tagging in hadron collisions. In this review, various state-of-the-art (SOTA) techniques in ML and DL are examined, with a focus on their implications for HEP demands. More precisely, this discussion addresses 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 highlights the challenges and potential areas for future research, which are illustrated for each application.

Problem

• Classify jets in high-energy physics using ML.
• Apply DL for image and point-cloud data analysis.
• Explore SOTA techniques for future collider applications.

Innovation

• Utilizes ML and DL for HEP
• Classifies jets in proton collisions
• Applies AI to image and PC data