mlsanityData to 7 Oct 2026

Generative Models for Fast Calorimeter Simulation.LHCb case

arXiv 1812.01319 ↗First posted 4 Dec 2018physics.data-an cs.LGcited by 25

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Abstract (arXiv)

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 order of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a big enough amount of simulated data needed by the next HL LHC experiments using limited computing resources.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 5; 2024: 4; 2025: 0; 2026: 4.

25 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  2. 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
  3. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  4. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  5. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  6. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
  7. 2023Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulationcited by 16
  8. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  9. 2023Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERNcited by 5
  10. 2023Artificial Intelligence for the Electron Ion Collider (AI4EIC)cited by 3
  11. 2026Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuningcited by 3
  12. 2026Generative Models and Statistical Validationcited by 2
13 more
  1. 2021Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulationscited by 1
  2. 2022Towards Reliable Neural Generative Modeling of Detectorscited by 1
  3. 2022Generative models uncertainty estimationcited by 1
  4. 2022Geometry-aware Autoregressive Models for Calorimeter Shower Simulationscited by 1
  5. 2023The LHCb ultra-fast simulation option, Lamarr: design and validationcited by 1
  6. 2024Particle physics DL-simulation with control over generated data propertiescited by 1
  7. 2024Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERNcited by 1
  8. 2024Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detectorcited by 1
  9. 2024Sequential Kernelized Stein Discrepancycited by 1
  10. 2021Accelerating GAN training using highly parallel hardware on public cloudcited by 0
  11. 2022Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulationscited by 0
  12. 2026ML-based Fast Simulation of FARICH Responsescited by 0
  13. 2026GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generationcited by 0