mlsanityData to 14 Oct 2026

Calorimeter shower superresolution

arXiv 2308.11700 ↗First posted 22 Aug 2023physics.ins-det cs.LG hep-excited by 7

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Abstract (arXiv)

Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline. There have been recent efforts to employ deep-generative surrogate models to overcome this challenge. However, many of best performing models have training and generation times that do not scale well to high-dimensional calorimeter showers. In this work, we introduce SuperCalo, a flow-based superresolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly upsampled from coarse-grained showers. This novel approach presents a way to reduce computational cost, memory requirements and generation time associated with fast calorimeter simulation models. Additionally, we show that the showers upsampled by SuperCalo possess a high degree of variation. This allows a large number of high-dimensional calorimeter showers to be upsampled from much fewer coarse showers with high-fidelity, which results in additional reduction in generation time.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 42 of its 88 references that are in the map. First posted on arXiv 22 Aug 2023.

Builds on

42 of 88 references.

References · 42

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30 more
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  2. 2015Cyclical Learning Rates for Training Neural Networkscited by 629
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  7. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
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  12. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
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  17. 2023Inductive Simulation of Calorimeter Showers with Normalizing Flowscited by 22
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  19. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
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  25. 2023nu2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flowscited by 10
  26. 2022CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulationcited by 6
  27. 2023Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweightingcited by 6
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 3.

7 papers.

  1. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  2. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  3. 2026AllShowers: One model for all calorimeter showerscited by 3
  4. 2026Generative Models and Statistical Validationcited by 2
  5. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  6. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1
  7. 2024Denoising Graph Super-Resolution towards Improved Collider Event Reconstructioncited by 0