Description
NO$\nu$A is a long-baseline neutrino oscillation experiment with the primary physics goals of precisely measuring the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$, determining the neutrino mass hierarchy, and constraining the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data currently relies on treating systematic uncertainties as nuisance parameters, which suffers from poor scalability as the number of uncertainties becomes larger. In this poster 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, and then considering them as an ensemble. PISCES has the advantage of supporting more complex fits, such as joint Near and Far Detector fits, and is ideal for inclusion of low-statistics samples in the fits. This poster focuses particularly on the conditional PISCES method, an efficient approach in which the Near Detector observation is used to create a conditional covariance matrix, constraining systematic uncertainties for a Far Detector only fit.
| Collaboration | NOvA Experiment |
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