mlsanityData to 14 Oct 2026

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation

arXiv 2203.08806 ↗First posted 15 Mar 2022hep-ph cs.LG hep-excited by 16

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Abstract (arXiv)

The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 38 of its 110 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

38 of 110 references.

References · 38

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26 more
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  2. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  3. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 1; 2025: 2; 2026: 2.

16 papers.

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  1. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 43
  2. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  3. 2023Inductive Simulation of Calorimeter Showers with Normalizing Flowscited by 22
  4. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  5. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  6. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  7. 2025OmniJet-αC: Learning point cloud calorimeter simulations using generative transformerscited by 14
  8. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  9. 2023PC-Droid: Faster diffusion and improved quality for particle cloud generationcited by 8
  10. 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
  11. 2023Calorimeter shower superresolutioncited by 7
  12. 2023Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physicscited by 6
4 more
  1. 2023ELSA -- Enhanced latent spaces for improved collider simulationscited by 5
  2. 2026Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuningcited by 3
  3. 2023High-precision regressors for particle physicscited by 0
  4. 2026GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generationcited by 0