Energy correlators can convert scaling violations into angular bump hunting for new physics, yielding projected competitive LHC sensitivity for a light hadrophilic Z'.
Jet substructure at the Large Hadron Collider: a review of recent advances in theory and machine learning
9 Pith papers cite this work. Polarity classification is still indexing.
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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.
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 0.19 while recovering Pomeron intercept of 1.0786.
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.
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 fast inference.
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 probes final-state hadronization.
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.
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 transverse-momentum regimes.
citing papers explorer
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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 0.19 while recovering Pomeron intercept of 1.0786.
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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 fast inference.
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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 probes final-state hadronization.
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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 transverse-momentum regimes.
- Looking inside jets: an introduction to jet substructure and boosted-object phenomenology