PISCES two-detector covariance matrix fit for the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors, a Near Detector (ND) at Fermilab, placed 1km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA’s primary physics goals are to measure the neutrino oscillation parameters θ₂₃ and Δm²₃₂ with high precision, determine the neutrino mass hierarchy, and constrain the value of δ_CP, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, which suffers from poor scalability as the number of uncertainties becomes larger. In this talk, we present PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES also has the advantage of supporting complex fits, such as a joint Near and Far detector fit. We will discuss the use of the PISCES two-detector fit in the context of the NOvA three-flavor analysis, including results from extensive robustness tests of the method and findings on the method’s performance.