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Machine learning assisted unfolding for neutrino cross-section measurements with the OmniFold technique

R. G. Huang1, A. Cudd2, M. Kawaue3, T. Kikawa3, B. Nachman1, V. Mikuni1, and C. Wilkinson1

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Phys. Rev. D 112, 012008 – Published 9 July, 2025

DOI: https://doi.org/10.1103/sp1f-n9k2

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.

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Phys. Rev. D 112, 012008– Published 9 July, 2025
  • Received 14 April 2025
  • Accepted 19 June 2025
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