mlsanityData to 7 Oct 2026

Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows

arXiv 2405.20407 ↗First posted 30 May 2024physics.ins-det cs.LG hep-excited by 13

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Abstract (arXiv)

In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best fidelity. However, as the latent space in such models is required to have the same dimensionality as the data space, scaling up normalizing flows to high dimensional datasets is not straightforward. The prior L2LFlows approach successfully used a series of separate normalizing flows and sequence of conditioning steps to circumvent this problem. In this work, we extend L2LFlows to simulate showers with a 9-times larger profile in the lateral direction. To achieve this, we introduce convolutional layers and U-Net-type connections, move from masked autoregressive flows to coupling layers, and demonstrate the successful modelling of showers in the ILD Electromagnetic Calorimeter as well as Dataset 3 from the public CaloChallenge dataset.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 60 of its 147 references that are in the map. First posted on arXiv 30 May 2024.

Builds on

60 of 147 references.

References · 60

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38 more
  1. 2016Revisiting Classifier Two-Sample Testscited by 258
  2. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  3. 2020Anomaly Detection with Density Estimationcited by 71
  4. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  5. 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
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  7. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
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  15. 2022CaloMan: Fast generation of calorimeter showers with density estimation on learned manifoldscited by 28
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50 most cited of 60 All 60 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 5.

13 papers.

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  1. 2025OmniJet-αC: Learning point cloud calorimeter simulations using generative transformerscited by 14
  2. 2025Foundation models for high-energy physicscited by 10
  3. 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
  4. 2026Scaling laws for amplitude surrogatescited by 5
  5. 2025Forecasting Generative Amplificationcited by 3
  6. 2026AllShowers: One model for all calorimeter showerscited by 3
  7. 2026Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuningcited by 3
  8. 2024Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Modelscited by 2
  9. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  10. 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogatescited by 2
  11. 2026Generative Models and Statistical Validationcited by 2
  12. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
1 more
  1. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1