mlsanityData to 14 Oct 2026

Simulation-Prior Independent Neural Unfolding Procedure

arXiv 2507.15084 ↗First posted 20 Jul 2025hep-ph cs.LG hep-excited by 4

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Abstract (arXiv)

Machine learning allows unfolding high-dimensional spaces without binning at the LHC. The new SPINUP method extracts the unfolded distribution based on a neural network encoding the forward mapping, making it independent of the prior from the simulated training data. It is made efficient through neural importance sampling, and ensembling can be used to estimate the effect of information loss in the forward process. We showcase SPINUP for unfolding detector effects on jet substructure observables and for unfolding to parton level of associated Higgs and single-top production.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 67 references that are in the map. First posted on arXiv 20 Jul 2025.

Builds on

13 of 67 references.

References · 13

  1. 2015Importance Weighted Autoencoderscited by 456
  2. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
  3. 2019How to GAN away Detector Effectscited by 28
  4. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22
  5. 2020Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inferencecited by 21
  6. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  7. 2024The Landscape of Unfolding with Machine Learningcited by 20
  8. 2023Improving Generative Model-based Unfolding with Schr"odinger Bridgescited by 13
  9. 2022nu-Flows: Conditional Neutrino Regressioncited by 11
  10. 2021Scaffolding Simulations with Deep Learning for High-dimensional Deconvolutioncited by 9
  11. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  12. 2024Generative Unfolding with Distribution Mappingcited by 6
1 more
  1. 2024Moment Unfoldingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Scaling laws for amplitude surrogatescited by 6
  2. 2026Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flowscited by 2
  3. 2026Factorizable Normalizing Flows for parameter-dependent density morphingcited by 1
  4. 2025Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameterscited by 0