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Machine Learning and LHC Event Generation
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First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.
Forward citations
Cited by 10 Pith papers
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Neural Control Variates at LO and NLO
Signed neural control variates from normalizing flows, combined with neural importance sampling, reduce weight ranges and negative weights for LO and NLO phase-space integration and event generation.
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Learning Standard Model structure from LHC data with Riemannian flow matching
ShellFlow, a Riemannian flow-matching transformer fed only on-shell and invariant-mass priors and ~8×10^8 recorded ATLAS events, reproduces the SM's dilepton resonances, Weinberg angle, and top/W mass peaks in a singl...
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Schr\"{o}dinger Generator for High-Dimensional Integration and Sampling on Quantum Many-Body States
A two-stage sampler (adaptive marginal map plus normalizing flow, then resampling) is proposed and shown on model nuclear densities up to D=624, though a core Jacobian equation appears sign-inconsistent.
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Agentic Re-Casting using Agentic Re-Simulations
An agentic AI system with a physicist in the loop re-casts an ATLAS ttZ measurement into a global top-quark SMEFT fit and recovers injected coloron Wilson coefficients in a repeatable benchmark.
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A universal vision transformer for fast calorimeter simulations
A vision-transformer flow-matching model generates calorimeter showers across regular and irregular detector geometries at millisecond speeds, and pretraining plus fine-tuning cuts training cost by about half.
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Simulation-Prior Independent Neural Unfolding Procedure
SPINUP is a neural-unfolding method that fits a parton-level generative model directly to detector-level data through a learned forward simulator, aiming to remove the simulation-prior bias.
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Explainable AI-assisted Optimization for Feynman Integral Reduction
FunSearch discovered a simple priority function for ordering IBP seeding integrals, reducing the number needed for multi-loop Feynman integral reductions by factors up to 3058.
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Unbinning global LHC analyses
Simulation-based inference produces stronger combined LHC constraints on SMEFT Wilson coefficients than histogram-based inference for four di-boson processes.
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HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency
HEPTAPOD uses LLM agents to drive FeynRules, MadGraph, Pythia, and analysis tools through schema-validated tool calls and run-card templates, demonstrated on a leptoquark signal scan.
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Machine Learning is Good for Physics - and Vice Versa
A perspective essay arguing that AI should be integrated into fundamental physics while preserving the field's statistical and theory-based standards, and that physics can enrich machine learning.
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