mlsanityData to 7 Oct 2026

How to GAN away Detector Effects

arXiv 1912.00477 ↗First posted 1 Dec 2019hep-ph cs.LGcited by 28

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Abstract (arXiv)

LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations, for instance, of detector effects can instead be inverted using generative networks. This allows us to reconstruct parton level information from measured events. Our results illustrate how, in general, fully conditional generative networks can statistically invert Monte Carlo simulations. As a technical by-product we show how a maximum mean discrepancy loss can be staggered or cooled.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 1; 2023: 5; 2024: 7; 2025: 2; 2026: 4.

28 papers.

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  1. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  2. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  3. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  4. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 27
  5. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22
  6. 2021Generative Networks for Precision Enthusiastscited by 21
  7. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  8. 2024The Landscape of Unfolding with Machine Learningcited by 20
  9. 2021Style-based quantum generative adversarial networks for Monte Carlo eventscited by 19
  10. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  11. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
  12. 2021Latent Space Refinement for Deep Generative Modelscited by 10
16 more
  1. 2021Scaffolding Simulations with Deep Learning for High-dimensional Deconvolutioncited by 9
  2. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  3. 2021A survey of machine learning-based physics event generationcited by 6
  4. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  5. 2024Generative Unfolding with Distribution Mappingcited by 6
  6. 2023Unbinned Profiled Unfoldingcited by 5
  7. 2026Scaling laws for amplitude surrogatescited by 5
  8. 2025Simulation-Prior Independent Neural Unfolding Procedurecited by 4
  9. 2023Generative Invertible Quantum Neural Networkscited by 3
  10. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  11. 2026Generative Models and Statistical Validationcited by 2
  12. 2026MIRA: A Score for Conditional Distribution Accuracy and Model Comparisoncited by 1
  13. 2023Designing Observables for Measurements with Deep Learningcited by 0
  14. 2024Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)cited by 0
  15. 2025Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameterscited by 0
  16. 2026Inverse Problems Conditioned on Observation Ensembles: Applications and Methodscited by 0