mlsanityData to 7 Oct 2026

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

arXiv 1701.05927 ↗First posted 20 Jan 2017stat.ML hep-ex physics.data-ancited by 46

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Abstract (arXiv)

We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. The pixel intensities of GAN-generated images faithfully span over many orders of magnitude and exhibit the desired low-dimensional physical properties (i.e., jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a novel empirical validation of image quality and validity of GAN-produced simulations of the natural world. This work provides a base for further explorations of GANs for use in faster simulation in High Energy Particle Physics.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2017.

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Citing papers per year: 2021: 16; 2022: 10; 2023: 3; 2024: 10; 2025: 6; 2026: 1.

46 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. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 45
  6. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 41
  7. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  8. 2022Evaluating generative models in high energy physicscited by 31
  9. 2021Generative Networks for Precision Enthusiastscited by 21
  10. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  11. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  12. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
34 more
  1. 2022Lorentz group equivariant autoencoderscited by 15
  2. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  3. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  4. 2021Optimising simulations for diphoton production at hadron colliders using amplitude neural networkscited by 9
  5. 2022Particle-based Fast Jet Simulation at the LHC with Variational Autoencoderscited by 7
  6. 2021Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Casecited by 6
  7. 2022Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spacescited by 6
  8. 2022CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulationcited by 6
  9. 2024PIPPIN: Generating variable length full events from partonscited by 6
  10. 2024Generating Physical Dynamics under Priorscited by 6
  11. 2024A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulationcited by 5
  12. 2024Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactionscited by 4
  13. 2025Generative Learning of Densities on Manifoldscited by 4
  14. 2021The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHCcited by 3
  15. 2021Graph Generative Models for Fast Detector Simulations in High Energy Physicscited by 2
  16. 2022GAN-based Matrix Factorization for Recommender Systemscited by 2
  17. 2022SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physicscited by 2
  18. 2023PE-GAN: Prior Embedding GAN for PXD images at Belle IIcited by 2
  19. 2024An information theoretic limit to data amplificationcited by 2
  20. 2025Even Faster Simulations with Flow Matching: A Study of Zero Degree Calorimeter Responsescited by 2
  21. 2025MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networkscited by 2
  22. 2021Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulationscited by 1
  23. 2022Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic fieldcited by 1
  24. 2024Examining Pathological Bias in a Generative Adversarial Network Discriminator: A Case Study on a StyleGAN3 Modelcited by 1
  25. 2024Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A surveycited by 1
  26. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  27. 2021Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modelingcited by 0
  28. 2021Accelerating GAN training using highly parallel hardware on public cloudcited by 0
  29. 2021Domain-informed neural networks for interaction localization within astroparticle experimentscited by 0
  30. 2022Exploring the Universality of Hadronic Jet Classificationcited by 0
  31. 2024Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletabilitycited by 0
  32. 2025Enabling Probabilistic Learning on Manifolds through Double Diffusion Mapscited by 0
  33. 2025Leveraging GNN to Enhance MEF Method in Predicting ENSOcited by 0
  34. 2026Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learningcited by 0