Energy Dependence in the Neutrino Scattering Data of MINERvA

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As we prepare for lower neutrino energy beams where physics is highly energy-dependent, it is important to isolate factors that can contribute largely to these low energy neutrino experiments. The world of neutrino oscillation experiments uses a wide range of energies: from 0.7 GeV beam of T2K and MicroBooNE, 2 GeV beam of NOvA, to the 0.6 to 6.0 GeV beam produced by DUNE in the future. MINERvA’s data are currently the best place to test the upper end of the range for DUNE and can be extrapolated into both DUNE’s and NOvA’s oscillation maximum. The goal of the research is to constrain energy dependence in the neutrino scattering experiment using data from MINERvA and then determine what factors contribute to the observed energy dependence. The research is divided into two parts. First, I will analyze different theoretical models in different channels of interaction, such as the quasi-elastic and delta resonance channels, in terms of neutrino energy dependence. The goal is to study the models at the level of the structure functions, which emphasizes the W2 structure function (the C function for QE) dominates the MINERvA data, but the W3 is increasingly important for NOvA and DUNE. The second half of the research is data-driven and focuses on the different detectors and systematic effects. An experimental effect, the angle acceptance, is larger than the structure function effects. However, it is a detector geometry effect and is well measured and well modeled. Other effects such as the muon energy scale are small and localized. Additionally, the research will determine if the MINERvA GENIE model correctly predicts all of the energy dependence seen in the data, and identify the remaining unmodeled energy dependence between the MINERvA data and its best simulation.

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University of Minnesota M.S. thesis. July 2021. Major: Physics. Advisor: Richard Gran. 1 computer file (PDF); viii, 117 pages.

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Mahbub, Ishmam. (2021). Energy Dependence in the Neutrino Scattering Data of MINERvA. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/224509.

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