Speaker
Description
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, as the kinematics of all outgoing particles in an event typically affects 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 collider and cosmology datasets. Here, we explore its use for neutrino physics using a public T2K near detector simulated dataset, and compare its performance with more traditional approaches, under a series of mock data studies.
