ALICE measures the radial jet-energy flow observable Δp_T in Pb-Pb (5.02 TeV) and pp (13 TeV) collisions and reports narrowing of the energy distribution in heavy-ion collisions at 3.5-4.5σ significance.
Kogleret al., Rev
12 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Jet substructure has emerged to play a central role at the Large Hadron Collider, where it has provided numerous innovative ways to search for new physics and to probe the Standard Model, particularly in extreme regions of phase space. In this article we focus on a review of the development and use of state-of-the-art jet substructure techniques by the ATLAS and CMS experiments.
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representative citing papers
RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
Scaling laws hold logarithmically for model size in autoregressive jet generation, with next-token loss correlating to physical metrics via sliced Wasserstein distance, but show weaker scaling for dataset size and compute due to rapid saturation.
Energy correlators can convert scaling violations into angular bump hunting for new physics, yielding projected competitive LHC sensitivity for a light hadrophilic Z'.
Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.
IRC-safe optimal-transport metrics (EMD, sEMD) enable lower-bias cell resampling of negative-weight NLO Monte Carlo events without intermediate jet clustering.
Projections of four-point energy correlators cleanly separate spin from kinematic azimuthal correlations inside jets; spin effects are subdominant in accessible LHC kinematics.
Spectral-function features fed to a dense network improve expected gluino-mass reach by ~150 GeV over ATLAS Gtt-0L-C and ~250 GeV over kinematics-only ML for the fully hadronic channel.
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}.
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.
Observing vector boson fusion production of a heavy scalar followed by decay to Z h1 at muon colliders can establish CP violation in the scalar sector because it requires both h2VV and h2h1Z couplings to be nonzero.
citing papers explorer
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Modification of jet-energy flow in heavy-ion collisions
ALICE measures the radial jet-energy flow observable Δp_T in Pb-Pb (5.02 TeV) and pp (13 TeV) collisions and reports narrowing of the energy distribution in heavy-ion collisions at 3.5-4.5σ significance.
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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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Neural Scaling Laws for Jet Generation
Scaling laws hold logarithmically for model size in autoregressive jet generation, with next-token loss correlating to physical metrics via sliced Wasserstein distance, but show weaker scaling for dataset size and compute due to rapid saturation.
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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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Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.
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Optimal-Transport-Based Cell Resampling for Negative and Pathological Event Weights
IRC-safe optimal-transport metrics (EMD, sEMD) enable lower-bias cell resampling of negative-weight NLO Monte Carlo events without intermediate jet clustering.
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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-function features fed to a dense network improve expected gluino-mass reach by ~150 GeV over ATLAS Gtt-0L-C and ~250 GeV over kinematics-only ML for the fully hadronic channel.
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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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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.
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Model-independent probes of CP violation in the heavy scalar sector at muon colliders
Observing vector boson fusion production of a heavy scalar followed by decay to Z h1 at muon colliders can establish CP violation in the scalar sector because it requires both h2VV and h2h1Z couplings to be nonzero.
- Looking inside jets: an introduction to jet substructure and boosted-object phenomenology