Image-Based Jet Analysis
This survey provides a comprehensive overview of image-based jet analysis for high energy physicists and machine learning researchers, detailing the current state and future directions of applying computer vision to jet data.
This paper surveys image-based jet analysis techniques, primarily focusing on convolutional neural networks for jet classification. It also reviews methods for understanding model learning, sensitivity to uncertainties, and successful applications at the LHC, alongside other applications like energy estimation and anomaly detection.
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.