mlsanityData to 7 Oct 2026

Generative Unfolding with Distribution Mapping

arXiv 2411.02495 ↗First posted 4 Nov 2024hep-ph cs.LG hep-excited by 6

Not on the map. Why?

Abstract (arXiv)

Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schr"odinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z + 2-jets.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 73 references that are in the map. First posted on arXiv 4 Nov 2024.

Builds on

23 of 73 references.

References · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2022Flow Matching for Generative Modelingcited by 5,426
  6. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  7. 2023I2SB: Image-to-Image Schr"odinger Bridgecited by 214
  8. 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 36
  9. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
  10. 2019How to GAN away Detector Effectscited by 28
  11. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 27
  12. 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
11 more
  1. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22
  2. 2020Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inferencecited by 21
  3. 2021Generative Networks for Precision Enthusiastscited by 21
  4. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  5. 2024The Landscape of Unfolding with Machine Learningcited by 20
  6. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
  7. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  8. 2023Improving Generative Model-based Unfolding with Schr"odinger Bridgescited by 13
  9. 2021Scaffolding Simulations with Deep Learning for High-dimensional Deconvolutioncited by 9
  10. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  11. 2023Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Informationcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Simulation-Prior Independent Neural Unfolding Procedurecited by 4
  2. 2026Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flowscited by 2
  3. 2026Generative Models and Statistical Validationcited by 2
  4. 2026Factorizable Normalizing Flows for parameter-dependent density morphingcited by 1
  5. 2025Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameterscited by 0
  6. 2026Inverse Problems Conditioned on Observation Ensembles: Applications and Methodscited by 0