mlsanityData to 7 Oct 2026

Inductive Simulation of Calorimeter Showers with Normalizing Flows

arXiv 2305.11934 ↗First posted 19 May 2023physics.ins-det cs.LG hep-excited by 22

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Abstract (arXiv)

Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy, but scaling this approach up to higher resolutions relevant for future detector upgrades leads to prohibitive memory constraints. To overcome this problem, we introduce Inductive CaloFlow (iCaloFlow), a framework for fast detector simulation based on an inductive series of normalizing flows trained on the pattern of energy depositions in pairs of consecutive calorimeter layers. We further use a teacher-student distillation to increase sampling speed without loss of expressivity. As we demonstrate with Datasets 2 and 3 of the CaloChallenge2022, iCaloFlow can realize the potential of normalizing flows in performing fast, high-fidelity simulation on detector geometries that are ~ 10 - 100 times higher granularity than previously considered.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

24 of 52 references.

References · 24

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2015Variational Inference with Normalizing Flowscited by 2,161
  4. 2016Density estimation using Real NVPcited by 2,029
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  6. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  7. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  8. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
  9. 2019Neural Spline Flowscited by 590
  10. 2015MADE: Masked Autoencoder for Distribution Estimationcited by 306
  11. 2016Revisiting Classifier Two-Sample Testscited by 258
  12. 2017Parallel WaveNet: Fast High-Fidelity Speech Synthesiscited by 214
12 more
  1. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  2. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  3. 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
  4. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  5. 2019Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physicscited by 42
  6. 2021CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 34
  7. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  8. 2022CaloMan: Fast generation of calorimeter showers with density estimation on learned manifoldscited by 28
  9. 2023Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoningcited by 25
  10. 2017Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeterscited by 23
  11. 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
  12. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 6; 2025: 6; 2026: 5.

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  1. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  2. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  3. 2023Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulationcited by 16
  4. 2024Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulationcited by 15
  5. 2025OmniJet-αC: Learning point cloud calorimeter simulations using generative transformerscited by 14
  6. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  7. 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
  8. 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
  9. 2023Calorimeter shower superresolutioncited by 7
  10. 2024Scaling Up Diffusion and Flow-based XGBoost Modelscited by 5
  11. 2026Scaling laws for amplitude surrogatescited by 5
  12. 2024Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactionscited by 4
10 more
  1. 2025Forecasting Generative Amplificationcited by 3
  2. 2025Inverse Autoregressive Flows for Zero Degree Calorimeter fast simulationcited by 3
  3. 2026AllShowers: One model for all calorimeter showerscited by 3
  4. 2026Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuningcited by 3
  5. 2024Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Modelscited by 2
  6. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  7. 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogatescited by 2
  8. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  9. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1
  10. 2026GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generationcited by 0