mlsanityData to 7 Oct 2026

GANplifying Event Samples

arXiv 2008.06545 ↗First posted 14 Aug 2020hep-ph hep-ex physics.data-ancited by 28

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Abstract (arXiv)

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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 1; 2023: 8; 2024: 5; 2025: 4; 2026: 4.

28 papers.

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  1. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  2. 2023EPiC-GAN: Equivariant Point Cloud Generation for Particle Jetscited by 23
  3. 2021Generative Networks for Precision Enthusiastscited by 21
  4. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  5. 2021Style-based quantum generative adversarial networks for Monte Carlo eventscited by 19
  6. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  7. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
  8. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  9. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
  10. 2025OmniJet-αC: Learning point cloud calorimeter simulations using generative transformerscited by 14
  11. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  12. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
16 more
  1. 2021Optimising simulations for diphoton production at hadron colliders using amplitude neural networkscited by 9
  2. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  3. 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
  4. 2021A survey of machine learning-based physics event generationcited by 6
  5. 2023Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweightingcited by 6
  6. 2024Calibrating Bayesian Generative Machine Learning for Bayesiamplificationcited by 5
  7. 2025Forecasting Generative Amplificationcited by 3
  8. 2026AllShowers: One model for all calorimeter showerscited by 3
  9. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  10. 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogatescited by 2
  11. 2026Generative Models and Statistical Validationcited by 2
  12. 2021Comparing Machine Learning and Interpolation Methods for Loop-Level Calculationscited by 1
  13. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1
  14. 2023High-precision regressors for particle physicscited by 0
  15. 2024Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)cited by 0
  16. 2026Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learningcited by 0