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GANplifying Event Samples

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arxiv 2008.06545 v3 pith:TPRR7KAL submitted 2020-08-14 hep-ph hep-exphysics.data-anstat.ML

classification hep-phhep-exphysics.data-anstat.ML
keywords eventeventsgenerativenetworkstrainingamplificationamplifyapplied
verification ladder T0 review T1 audit T2 compute T3 formal
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A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.

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Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neural Control Variates at LO and NLO

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  2. Agentic Re-Casting using Agentic Re-Simulations

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    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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  4. A universal vision transformer for fast calorimeter simulations

    hep-ph 2026-01 conditional novelty 6.0 of 10

    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.

  5. GPT-like transformer model for silicon tracking detector simulation

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    A decoder-only transformer trained on tokenized Geant4 hit sequences generates silicon tracker hits that reconstruct to near-Geant4-quality tracks for single muons.

  6. Forecasting Generative Amplification

    hep-ph 2025-09 conditional novelty 6.0 of 10

    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.

  7. ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation

    hep-ph 2025-02 conditional novelty 6.0 of 10

    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.

  8. Machine Learning is Good for Physics - and Vice Versa

    hep-ph 2026-08 unverdicted novelty 3.0 of 10

    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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