Neutrino-Nucleus Cross Section Impacts on Neutrino Oscillation Measurements

TL;DR

This paper assesses how uncertainties in neutrino-nucleus cross section modeling affect neutrino oscillation measurements, highlighting potential biases in future experiments and emphasizing the need for improved models and mitigation strategies.

Contribution

It provides a comprehensive, simulation-based evaluation of cross section uncertainties' impact on oscillation parameters for NOvA and DUNE-like setups, using multiple event generators.

Findings

Cross section uncertainties have minimal impact on current experiments like NOvA.

Future experiments like DUNE could experience significant biases due to these uncertainties.

Near-detector data alone may not fully mitigate cross section uncertainties in future high-precision experiments.

Abstract

The challenges in neutrino-nucleus cross section modeling and its impact on neutrino oscillation experiments are widely recognized. However, a comprehensive and theoretically robust estimation of cross section uncertainties has been lacking, and few studies have quantitatively examined their impact on oscillation measurements. In this work, we evaluate the effect of cross section uncertainties on oscillation parameters using setups inspired by NOvA and DUNE. To characterize these uncertainties, we adopt multiple neutrino-nucleus event generators and simulate a realistic experimental procedure that incorporates near-detector data and near-to-far-detector extrapolation. Our results confirm that cross section uncertainties cannot significantly bias oscillation results in current statistics-dominated experiments like NOvA. However, they could lead to substantial bias for future systematics-dominated experiments like DUNE, even when near-detector data are employed to mitigate uncertainties. These findings underscore the need for further studies on the quantitative impacts of cross section modeling, improved strategies to utilize near-detector data and the PRISM concept, and more robust cross section models to optimize the success of future experiments.