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Event Generation with Normalizing Flows
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We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at next-to-leading order QCD.
Forward citations
Cited by 5 Pith papers
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Resonance-aware parton-shower matching for off-shell top-antitop production with semi-leptonic decays at electron-positron colliders
A resonance-aware MC@NLO matching procedure preserves top-antitop line shapes when NLO QCD predictions for off-shell ttbar production at e+e− colliders are showered with Pythia8.
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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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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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ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation
ARCANE reweighting adds a carefully designed, zero-average correction to event weights so that positive and negative pathways to the same event cancel, preserving all physical distributions.
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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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