Confidence interval estimation for NOvA's oscillation measurements using supercomputers

NOvA is a long-baseline neutrino oscillation experiment. It measures both the disappearance of muon neutrinos and the appearance of electron neutrinos at a large distance from a source of muon neutrinos in order to constrain neutrino oscillation parameters. In particular, NOvA aims to precisely measure $\theta_{23}$ and $\Delta{}m^{2}_{32}$, as well as determine the neutrino mass hierarchy and probe the existence of CP-violation in the neutrino sector. NOvA is a low-statistics experiment and the neutrino oscillation model contains several physical boundaries. Constructing statistically correct confidence intervals is therefore challenging. NOvA ensures a correct statistical coverage by following the computationally expensive Feldman-Cousins prescription. This approach requires the generation and fitting of millions of pseudo-experiments to build empirical test-statistics distributions. This talk will describe this technique and the framework implemented on supercomputers that reduced the time necessary to produce statistically robust results from several months down to a few days.