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CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows

arXiv 2106.05285 ↗First posted 9 Jun 2021physics.ins-det cs.LG hep-excited by 46

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Abstract (arXiv)

We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers with extremely high fidelity, providing a fresh alternative to computationally expensive GEANT4 simulations, as well as other state-of-the-art fast simulation frameworks based on GANs and VAEs. Besides the usual histograms of physical features and images of calorimeter showers, we introduce a new metric for judging the quality of generative modeling: the performance of a classifier trained to differentiate real from generated images. We show that GAN-generated images can be identified by the classifier with nearly 100% accuracy, while images generated from CaloFlow are better able to fool the classifier. More broadly, normalizing flows offer several advantages compared to other state-of-the-art approaches (GANs and VAEs), including: tractable likelihoods; stable and convergent training; and principled model selection. Normalizing flows also provide a bijective mapping between data and the latent space, which could have other applications beyond simulation, for example, to detector unfolding.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 41 of its 99 references that are in the map. First posted on arXiv 9 Jun 2021.

Builds on

41 of 99 references.

References · 41

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2017On Calibration of Modern Neural Networkscited by 4,860
  4. 2017Improved Training of Wasserstein GANscited by 2,223
  5. 2015Variational Inference with Normalizing Flowscited by 2,161
  6. 2016Density estimation using Real NVPcited by 2,029
  7. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  8. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  9. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  10. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
  11. 2017Wasserstein GANcited by 635
  12. 2019Neural Spline Flowscited by 590
29 more
  1. 2015MADE: Masked Autoencoder for Distribution Estimationcited by 306
  2. 2016Revisiting Classifier Two-Sample Testscited by 258
  3. 2017Parallel WaveNet: Fast High-Fidelity Speech Synthesiscited by 214
  4. 2020Flows for simultaneous manifold learning and density estimationcited by 117
  5. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  6. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  7. 2020Anomaly Detection with Density Estimationcited by 71
  8. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  9. 2017Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesiscited by 46
  10. 2019Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physicscited by 42
  11. 2019How to GAN away Detector Effectscited by 28
  12. 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
  13. 2020i-flow: High-dimensional Integration and Sampling with Normalizing Flowscited by 26
  14. 2018Generative Models for Fast Calorimeter Simulation.LHCb casecited by 25
  15. 2017Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeterscited by 23
  16. 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
  17. 2019Information bottleneck through variational glassescited by 19
  18. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
  19. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15
  20. 2019Lund jet images from generative and cycle-consistent adversarial networkscited by 11
  21. 2020Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physicscited by 11
  22. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  23. 2019Cherenkov Detectors Fast Simulation Using Neural Networkscited by 8
  24. 2020Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networkscited by 8
  25. 2018Image-based model parameter optimization using Model-Assisted Generative Adversarial Networkscited by 7
  26. 2018GANs for generating EFT modelscited by 6
  27. 2021Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Casecited by 6
  28. 2020Exhaustive Neural Importance Sampling applied to Monte Carlo event generationcited by 3
  29. 2021The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHCcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 9; 2023: 17; 2024: 7; 2025: 6; 2026: 4.

46 papers.

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  1. 2021CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 34
  2. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  3. 2022Evaluating generative models in high energy physicscited by 31
  4. 2022CaloMan: Fast generation of calorimeter showers with density estimation on learned manifoldscited by 28
  5. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  6. 2023EPiC-GAN: Equivariant Point Cloud Generation for Particle Jetscited by 23
  7. 2023Inductive Simulation of Calorimeter Showers with Normalizing Flowscited by 22
  8. 2021Generative Networks for Precision Enthusiastscited by 21
  9. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  10. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  11. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  12. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
34 more
  1. 2023Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulationcited by 16
  2. 2022CaloFlow for CaloChallenge Dataset 1cited by 14
  3. 2025OmniJet-αC: Learning point cloud calorimeter simulations using generative transformerscited by 14
  4. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  5. 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
  6. 2022nu-Flows: Conditional Neutrino Regressioncited by 11
  7. 2023Goodness of fit by Neyman-Pearson testingcited by 10
  8. 2023PC-Droid: Faster diffusion and improved quality for particle cloud generationcited by 8
  9. 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
  10. 2022Particle-based Fast Jet Simulation at the LHC with Variational Autoencoderscited by 7
  11. 2023Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Informationcited by 7
  12. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  13. 2022Flowification: Everything is a Normalizing Flowcited by 5
  14. 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
  15. 2023ELSA -- Enhanced latent spaces for improved collider simulationscited by 5
  16. 2023Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulationcited by 5
  17. 2024A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulationcited by 5
  18. 2024Scaling Up Diffusion and Flow-based XGBoost Modelscited by 5
  19. 2021Funnels: Exact maximum likelihood with dimensionality reductioncited by 4
  20. 2024Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactionscited by 4
  21. 2023Artificial Intelligence for the Electron Ion Collider (AI4EIC)cited by 3
  22. 2025Forecasting Generative Amplificationcited by 3
  23. 2025SURFing to the Fundamental Limit of Jet Taggingcited by 3
  24. 2022SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physicscited by 2
  25. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  26. 2025Even Faster Simulations with Flow Matching: A Study of Zero Degree Calorimeter Responsescited by 2
  27. 2026BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulationcited by 2
  28. 2022Geometry-aware Autoregressive Models for Calorimeter Shower Simulationscited by 1
  29. 2024Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactionscited by 1
  30. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  31. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1
  32. 2023High-precision regressors for particle physicscited by 0
  33. 2026ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physicscited by 0
  34. 2026Learning Standard Model structure from LHC data with Riemannian flow matchingcited by 0