Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning

TL;DR

This review covers recent theoretical and machine learning advancements in jet substructure analysis at the LHC, highlighting new observables, techniques, and their applications in probing the Standard Model and searching for new physics.

Contribution

It provides a comprehensive overview of the latest developments in jet substructure theory and machine learning, serving as both an educational resource and a reference for experts.

Findings

Introduction of new jet substructure observables

Application of advanced machine learning techniques

Enhanced methods for probing the Standard Model and new physics

Abstract

Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC), where it has provided numerous innovative new ways to search for new physics and to probe the Standard Model in extreme regions of phase space. In this article we provide a comprehensive review of state of the art theoretical and machine learning developments in jet substructure. This article is meant both as a pedagogical introduction, covering the key physical principles underlying the calculation of jet substructure observables, the development of new observables, and cutting edge machine learning techniques for jet substructure, as well as a comprehensive reference for experts. We hope that it will prove a useful introduction to the exciting and rapidly developing field of jet substructure at the LHC.