Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique
Abstract
The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section modeling uncertainties in the analysis. A key issue is the dimensionality used in unfolding, as the kinematics of all outgoing particles in an event typically affect the reconstruction performance in a neutrino detector. OmniFold is an unfolding method that iteratively reweights a simulated dataset, using machine learning to utilize arbitrarily high-dimensional information, that has previously been applied to proton-proton and proton-electron datasets. This paper demonstrates OmniFold's application to a neutrino cross-section measurement for the first time using a public T2K near detector simulated dataset, comparing its performance with traditional approaches using a mock data study.
Cite
@article{arxiv.2504.06857,
title = {Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique},
author = {Roger G. Huang and Andrew Cudd and Masaki Kawaue and Tatsuya Kikawa and Benjamin Nachman and Vinicius Mikuni and Callum Wilkinson},
journal= {arXiv preprint arXiv:2504.06857},
year = {2025}
}
Comments
16 pages, 12 figures, 4 tables