The Korean Physical Society 06130 22, Teheran-ro 7-gil, Gangnam-gu, Seoul, Republic of Korea 610 Representation : Tae Won NOH TEL: 02-556-4737 FAX: 02-554-1643 E-mail : webmaster@kps.or.kr Copyright(C) KPS, All rights reserved.
30 May 2022 to 4 June 2022
Virtual Seoul
Asia/Seoul timezone

Constraining NOvA physics model parameters with the Near Detector using Frequentist and Bayesian statistical techniques

Not scheduled
5m
Virtual Seoul

Virtual Seoul

Poster Neutrino interactions Poster

Description

The NuMI Off-Axis Neutrino Appearance (NOvA) experiment is an 810 km baseline neutrino oscillation experiment measuring the fundamental properties of neutrinos and antineutrinos, using the high statistics data from the Near Detector (ND) at Fermilab to produce predictions for the Far Detector (FD) in Minnesota. In measurements published so far, NOvA has compared the Far Detector data to an energy spectrum obtained from a data-driven prediction method based Near Detector (ND) data called extrapolation. This poster presents an alternate approach in which physics model parameters in the NOvA simulation are directly constrained using the ND data via two different methods: one utilizing Bayesian statistics and the other with Frequentist statistics. The neutrino and antineutrino simulation is divided into subsets based on multiplicity and topology of visible particles. These are input to a Bayesian Markov Chain Monte Carlo (MCMC) and a Frequentist Poisson maximum likelihood fitting procedure. This work demonstrates these two fitting methods effectively constrain the model uncertainties with pseudodata generated by randomly varying physics parameters in the ND simulation. This is an initial step towards achieving a two-detector fit by constraining NOvA's physics model parameters to measure the neutrino oscillation parameters, $sin^{2}(\theta_{23})$, $\Delta m_{23}^{2}$, and $\delta_{CP}$ with its data.

Collaboration NOvA

Primary authors

Michael Dolce (Tufts University) Maria Martinez Casales (Iowa State University)

Presentation Materials

There are no materials yet.