Image-Based Jet Analysis

TL;DR

This paper surveys the use of jet images and deep learning, especially CNNs, for jet analysis in high energy physics, covering classification, understanding models, and real-world applications at the LHC.

Contribution

It provides a comprehensive review of jet image analysis techniques, including recent advances and applications in experimental high energy physics.

Findings

CNN-based models improve jet classification accuracy

Jet image techniques enable new physics analyses

Applications include energy estimation and anomaly detection

Abstract

Image-based jet analysis is built upon the jet image representation of jets that enables a direct connection between high energy physics and the fields of computer vision and deep learning. Through this connection, a wide array of new jet analysis techniques have emerged. In this text, we survey jet image based classification models, built primarily on the use of convolutional neural networks, examine the methods to understand what these models have learned and what is their sensitivity to uncertainties, and review the recent successes in moving these models from phenomenological studies to real world application on experiments at the LHC. Beyond jet classification, several other applications of jet image based techniques, including energy estimation, pileup noise reduction, data generation, and anomaly detection, are discussed.