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How to GAN LHC Events
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Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair production we show how such a network describes intermediate on-shell particles, phase space boundaries, and tails of distributions. In particular, we introduce the maximum mean discrepancy to resolve sharp local features. It can be extended in a straightforward manner to include for instance off-shell contributions, higher orders, or approximate detector effects.
Forward citations
Cited by 9 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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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
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Agentic Re-Casting using Agentic Re-Simulations
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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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Forecasting Generative Amplification
A KS-test-based differential method and a Bayesian averaging method estimate generative amplification without holdout datasets and find amplification in selected LHC phase-space regions.
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How to Unfold Top Decays
A conditional flow-matching network with batch-level conditioning and multi-mass training unfolds top-decay kinematics and extracts the top mass with reduced model bias.
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Lund jet images from generative and cycle-consistent adversarial networks
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Exploring anomalous couplings in Higgs boson pair production through shape analysis
Anomalous Higgs couplings change the shape of the di-Higgs mass distribution, and an unsupervised clustering algorithm captures those shape differences more finely than a hand-defined taxonomy.
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