Measuring Neutrino Oscillations with NOvA and T2K

Abstract

The discoveries of the twentieth century proved that neutrinos have mass and can change flavor. For the past few decades, a major focus of research has been the measurement of the physical parameters which govern this flavor oscillation. These measurements remain inconclusive on a few key questions, including the ordering of the neutrino masses and whether neutrinos violate CP symmetry. NOvA and T2K are two long-baseline accelerator neutrino experiments working in this space. By placing detectors in a beam of muon (anti-)neutrinos, these experiments interrogate neutrino oscillations by measuring muon (anti-)neutrino disappearance and electron (anti-)neutrino appearance. The complementarity of NOvA's and T2K's oscillation measurements motivated the experiments to pursue a joint oscillation analysis. After bracketing the potential impacts of correlations between the two experiments' systematic uncertainties and constructing a joint likelihood function, we share in this thesis the first results from the NOvA-T2K joint oscillation analysis. We report the world's most precise measurement of Δm232 to date: +2.429+0.039-0.035 (-2.477±0.035) × 10-3 eV2 assuming the normal (inverted) mass ordering, showing a slight preference for the inverted mass ordering. The maximally CP-violating value of δCP=+π/2 is excluded by 3σ credible intervals, and if we assume neutrinos are in the inverted mass ordering, we see evidence of CP violation at 3σ. Additionally, we present an effort to encapsulate neutrino cross-section models in a parametrization-agnostic way. We have created a suite of systematic parameters that are capable of mimicking the action of NOvA's cross-section model. This method could be used in a future joint data analysis between long-baseline neutrino experiments. We also introduce Voronoi histograms, an ancillary technique developed as part of this program. Voronoi histograms are a new way to efficiently bin high-dimensional data. This method preserves bin density in regions of interest, while tightly controlling the total number of bins used. The performance gains from using Voronoi binnings over standard rectangular binnings scale dramatically with the dimensionality of the data