mlsanityData to 7 Oct 2026

End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

arXiv 2305.10399 ↗First posted 17 May 2023hep-ex cs.LGcited by 22

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors. Methods to solve this textitinverse problem of mapping detector observations to theoretical quantities of the underlying collision are essential parts of many physics analyses at the LHC. We investigate and compare various generative deep learning methods to approximate this inverse mapping. We introduce a novel unified architecture, termed latent variation diffusion models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. We demonstrate the effectiveness of this approach for reconstructing global distributions of theoretical kinematic quantities, as well as for ensuring the adherence of the learned posterior distributions to known physics constraints. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline and 3 times less than traditional latent diffusion models.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 31 of its 61 references that are in the map. First posted on arXiv 17 May 2023.

Builds on

31 of 61 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design11 Oct
  2. 2022Score-based Generative Models for Calorimeter Shower Simulation17 Jun

Other references · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  7. 2016Layer Normalizationcited by 5,579
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  9. 2022A ConvNet for the 2020scited by 3,545
  10. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  11. 2021Variational Diffusion Modelscited by 1,145
  12. 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
17 more
  1. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  2. 2021Score-based Generative Modeling in Latent Spacecited by 634
  3. 2015Generating Sentences from a Continuous Spacecited by 607
  4. 2019The frontier of simulation-based inferencecited by 490
  5. 2016Ladder Variational Autoencoderscited by 315
  6. 2019Stabilizing Transformers for Reinforcement Learningcited by 262
  7. 2022DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latentscited by 103
  8. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  9. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  10. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
  11. 2019How to GAN away Detector Effectscited by 28
  12. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  13. 2020Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networkscited by 22
  14. 2021Generative Networks for Precision Enthusiastscited by 21
  15. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  16. 2021SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attentioncited by 18
  17. 2023Unbinned Profiled Unfoldingcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 10; 2025: 4; 2026: 2.

22 papers.

Sort:

By area:

  1. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  2. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 27
  3. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 22
  4. 2024The Landscape of Unfolding with Machine Learningcited by 20
  5. 2024Diffusion State-Guided Projected Gradient for Inverse Problemscited by 14
  6. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  7. 2023PC-Droid: Faster diffusion and improved quality for particle cloud generationcited by 8
  8. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  9. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  10. 2024Generative Unfolding with Distribution Mappingcited by 6
  11. 2023Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulationcited by 5
  12. 2024Towards diffusion models for large-scale sea-ice modellingcited by 4
10 more
  1. 2025Simulation-Prior Independent Neural Unfolding Procedurecited by 4
  2. 2024Latent Representation Matters: Human-like Sketches in One-shot Drawing Taskscited by 3
  3. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  4. 2026Generative Models and Statistical Validationcited by 2
  5. 2024Variational inference for pile-up removal at hadron colliders with diffusion modelscited by 1
  6. 2025Latent Generative Modeling of Random Fields from Limited Training Datacited by 1
  7. 2023Designing Observables for Measurements with Deep Learningcited by 0
  8. 2025Towards Photonic Band Diagram Generation with Transformer-Latent Diffusion Modelscited by 0
  9. 2025Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameterscited by 0
  10. 2026Diffusion-Driven State Space Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN
  2. 2025Multimodal Generative Flows for LHC Jets
  3. 2026Generative models on phase space
  4. 2026Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation
  5. 2025Adaptive conditional latent diffusion maps beam loss to 2D phase space projections
  6. 2025CaloHadronic: a diffusion model for the generation of hadronic showers
  7. 2025Jet Image Generation in High Energy Physics Using Diffusion Models
  8. 2025Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation