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

TL;DR

This review surveys the rapid progress in jet substructure at the LHC from both theory and machine learning perspectives. It synthesizes the analytic framework for calculating jet observables, including resummation, fixed-order corrections, non-perturbative effects, and grooming, with an emphasis on prong-structure observables and new frontiers like NGLs and jet-radius logarithms. It then surveys a broad spectrum of ML approaches for jet representations, tagging, calibration, simulation, and anomaly detection, highlighting how data-driven methods complement and extend traditional QCD techniques. The article also discusses data openness, physics-driven observable design, and data-theory collaboration paths, aiming to guide precise QCD studies and robust jet tagging in the LHC era and beyond.

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.

Figures (34)

• Figure 1: A typical jet substructure calculation, emphasizing the regions where different contributions dominate the physical description of the observable. Here, $\sigma_\text{f.o.}$ is the fixed-order prediction for the cross section, $\sigma_\text{res}$ includes resummation of large logarithms and $\sigma$ is the complete theory prediction including non-perturbative effects through a shape function $F_\text{np}$.
• Figure 2: The different scales relevant for the description of a mass measurement on a jet. Sudakov double logarithms, $\log^2 (p_{TJ}/m_J)$, are resummed by renormalization group evolution between the different scales.
• Figure 3: A schematic of the factorization employed in a parton shower program into a hard scattering, denoted $Q_{\text{UV}}$, a shower describing perturbative soft and collinear emissions, and an exclusive hadronization model, describing the conversion into hadrons. Taken from Ref. Skands:2012ts
• Figure 4: The configuration of the final state partons in a parton shower based on a Markovian implementation of $2\to 3$ splitting. Approximations for the splitting kernel are made throughout the entire phase space, based on the soft and collinear limits. Taken from Ref. Giele:2011cb
• Figure 5: Differential cross section of the angularity $\tau^{(3/2)}$ for both quark (blue) and gluon (red) jets produced in $e^+e^-$ collisions. NLL analytic predictions are solid, and Pythia predictions are the histograms with parton level (solid) and hadron level (dashed). Adapted from Ref. Ellis:2010rwa.