Developing Multidimensional Voronoi Histograms for NOvA's Physics Program

In instances where computational time and complexity scale with the number of bins in a histogram, the curse of dimensionality can make it difficult to choose a binning for data in more than a few dimensions. In one dimension, variable-width bins preserve resolution in regions of interest while reducing resolution in other regions to limit the total number of bins used to capture the data. In this talk, I will show a novel technique to extend the concept of variable bin widths to higher dimensions using Voronoi diagrams. I will also show applications of this technique to produce histograms useful for tuning cross-section models in NOvA and for the NOvA multidetector oscillation fit program.