Abstract:  NOvA is a neutrino oscillation experiment designed to measure and constrain several of the oscillation parameters of the PMNS matrix (\Delta M_{32}^{2}, \theta_{23}, and \delta_{CP}). The traditional NOvA analysis methodology is to measure the oscillation parameters through an “extrapolation” procedure where the Near Detector (ND) data-MC discrepancy serves as a correction to the Far Detector (FD) MC. This talk introduces a novel means of measuring the oscillation parameters and constraining the NOvA uncertainty model by simultaneously fitting the ND and FD simulation with the Bayesian inference tool Markov Chain Monte Carlo (MCMC). With this methodology, the power of constraining the NOvA uncertainties stems from the statistical strength of the ND dataset. This talk will focus on the developments made to perform an effective ND fit to NOvA data, while also highlighting an improved understanding of NOvA’s ND MC simulation. At the end, I will discuss the ongoing work to develop machinery and incorporate FD MC for a simultaneous ND + FD joint fit.

Small Seminar Room