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Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
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Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
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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.
Forward citations
Cited by 16 Pith papers
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Energy-energy correlators in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV
First measurement of two-point EEC in 20-80 GeV/c charged jets in p-Pb collisions shows modification relative to pp at the same energy, with enhancement at large angles and suppression at small angles in the 20-40 GeV...
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Reweighting Adversarial Networks for Unbinned Unfolding
RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
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Bump Hunting Inside Jets with Energy Correlators
Energy correlators can convert scaling violations into angular bump hunting for new physics, yielding projected competitive LHC sensitivity for a light hadrophilic Z'.
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Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
A MERA-based autoencoder supplies a locality-aware hierarchical inductive bias that improves reconstruction-based anomaly detection for collider jets, with disentanglers providing benefit at strong compression bottlenecks.
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Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.1...
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Full energy fraction and angular dependence of medium-induced splittings in the large-$N_c$ limit
In large-Nc and harmonic oscillator limits, medium-induced splittings are computed analytically double-differential in z and θ, with an improved semi-hard approximation validated for high-energy partons.
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OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers
OmniMol transfers a billion-jet pre-trained PET foundation model from HEP to molecular dynamics via an interaction-matrix attention bias, delivering strong performance on the oMol dataset with minimal fine-tuning and ...
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Jet Charge with Global Event Shapes: Probing Quark Flavor Dynamics
A factorization theorem is derived for the joint measurement of 1-jettiness and jet charge in DIS, introducing a new universal charged jet function that enhances quark flavor separation in initial-state PDFs and probe...
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Dissecting Parton Showers with Multi-Point Energy Correlators
Projections of four-point energy correlators cleanly separate spin from kinematic azimuthal correlations inside jets; spin effects are subdominant in accessible LHC kinematics.
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Machine learning fully hadronic events with spectral functions
Spectral functions from two-point correlations serve as multiplicity-independent ML inputs and improve expected gluino mass reach by 150-250 GeV in a fully hadronic ttbar vs gluino benchmark.
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"Hadron-in-fat-jet'' AI Tagging to Detect Rare Decays Such as $W^{\pm}\to\pi^{\pm}\gamma$
Proof-of-principle for hadron-in-fat-jet AI tagging that yields an expected 95% CL limit of B(W±→π±γ) < 2.78×10^{-5} at 450 fb^{-1}.
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AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements
AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.
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Alignment and Enhanced Multi-Higgs Production
In extended scalar sectors near the alignment limit, higher-dimensional interactions can make two-, three-, or four-Higgs final states the dominant discovery mode at the LHC via gluon fusion.
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Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across...
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QCD for electroweak precision measurements: Foundations
QCD collinear factorization, DGLAP evolution, and QT resummation, developed via DIS and Drell-Yan, form the calculational foundation for precision electroweak observables and new-physics searches.
- Looking inside jets: an introduction to jet substructure and boosted-object phenomenology
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