2025/04/09 by Huang, Roger G., Cudd, Andrew, Kawaue, Masaki +4 · 4 citations
#Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2504.06857
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.