mlsanityData to 7 Oct 2026

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders

arXiv 2203.00520 ↗First posted 1 Mar 2022physics.comp-ph cs.LG hep-excited by 7

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Abstract (arXiv)

We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a Deep Variational Autoencoder to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 59 references that are in the map. First posted on arXiv 1 Mar 2022.

Builds on

17 of 59 references.

References · 17

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5 more
  1. 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
  2. 2019Lund jet images from generative and cycle-consistent adversarial networkscited by 11
  3. 2020Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physicscited by 11
  4. 2020Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learningcited by 5
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 0; 2025: 0; 2026: 1.

7 papers.

  1. 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
  2. 2022Evaluating generative models in high energy physicscited by 31
  3. 2023EPiC-GAN: Equivariant Point Cloud Generation for Particle Jetscited by 23
  4. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  5. 2022Lorentz group equivariant autoencoderscited by 15
  6. 2023Versatile Energy-Based Probabilistic Models for High Energy Physicscited by 2
  7. 2026Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthroughcited by 1