DescriptionWhen fitting to data with low statistics and near physical boundaries, extra measures need to be taken to ensure proper statistical coverage. The method NOvA uses is called the Feldman-Cousins procedure, which entails fitting thousands of independent pseudoexperiments to generate acceptance intervals that are then used to correct our fits. The scale required by the Feldman-Cousins procedure makes it extremely computationally intensive. In past analyses, it has taken up to several weeks to complete, bottlenecking our final results. Here, I present recent work by members of the NOvA experiment and the SciDAC4 collaboration to enable the use of the supercomputing facilities at NERSC to process our Feldman-Cousins corrections over 50x faster, allowing us to perform more studies, increase the precision of our fits, and produce results quickly.