# OmniFold: A Method to Simultaneously Unfold All Observables

arXiv: https://arxiv.org/abs/1911.09107 · HTML: https://mlsanity.com/paper/1911.09107/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 30 of 31)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Learning Diffusion Priors from Observations by Expectation Maximization](https://mlsanity.com/paper/2405.13712/) (2024)
- [End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics](https://mlsanity.com/paper/2305.10399/) (2023)
- [An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training](https://mlsanity.com/paper/2212.08674/) (2022)
- [The Landscape of Unfolding with Machine Learning](https://mlsanity.com/paper/2404.18807/) (2024)
- [E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once](https://mlsanity.com/paper/2101.07263/) (2021)
- [Learning Likelihood Ratios with Neural Network Classifiers](https://mlsanity.com/paper/2305.10500/) (2023)
- [Latent Space Refinement for Deep Generative Models](https://mlsanity.com/paper/2106.00792/) (2021)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [nu2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows](https://mlsanity.com/paper/2307.02405/) (2023)
- [Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution](https://mlsanity.com/paper/2105.04448/) (2021)
- [Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion](https://mlsanity.com/paper/2404.14332/) (2024)
- [Unifying Simulation and Inference with Normalizing Flows](https://mlsanity.com/paper/2404.18992/) (2024)
- [Generative Unfolding with Distribution Mapping](https://mlsanity.com/paper/2411.02495/) (2024)
- [Scaling laws for amplitude surrogates](https://mlsanity.com/paper/2601.13308/) (2026)
- [Unbinned Profiled Unfolding](https://mlsanity.com/paper/2302.05390/) (2023)
- [ELSA -- Enhanced latent spaces for improved collider simulations](https://mlsanity.com/paper/2305.07696/) (2023)
- [Simulation-Prior Independent Neural Unfolding Procedure](https://mlsanity.com/paper/2507.15084/) (2025)
- [Artificial Intelligence for the Electron Ion Collider (AI4EIC)](https://mlsanity.com/paper/2307.08593/) (2023)
- [BUFF: Boosted Decision Tree based Ultra-Fast Flow matching](https://mlsanity.com/paper/2404.18219/) (2024)
- [ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset](https://mlsanity.com/paper/2512.15230/) (2025)
- [Multidimensional Deconvolution with Profiling](https://mlsanity.com/paper/2409.10421/) (2024)
- [Novel machine learning applications at the LHC](https://mlsanity.com/paper/2409.20413/) (2024)
- [An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning](https://mlsanity.com/paper/2607.21702/) (2026)
- [Designing Observables for Measurements with Deep Learning](https://mlsanity.com/paper/2310.08717/) (2023)
- [Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters](https://mlsanity.com/paper/2512.07074/) (2025)
- [Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function](https://mlsanity.com/paper/2512.19913/) (2025)
- [Inverse Problems Conditioned on Observation Ensembles: Applications and Methods](https://mlsanity.com/paper/2601.22029/) (2026)
- [Unfolding with a Wasserstein Loss](https://mlsanity.com/paper/2603.20903/) (2026)
- [ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics](https://mlsanity.com/paper/2604.01313/) (2026)

## Abstract (arXiv)

Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments. Unfolding is traditionally done for individual, binned observables without including all information relevant for characterizing the detector response. We introduce OmniFold, an unfolding method that iteratively reweights a simulated dataset, using machine learning to capitalize on all available information. Our approach is unbinned, works for arbitrarily high-dimensional data, and naturally incorporates information from the full phase space. We illustrate this technique on a realistic jet substructure example from the Large Hadron Collider and compare it to standard binned unfolding methods. This new paradigm enables the simultaneous measurement of all observables, including those not yet invented at the time of the analysis.