Measurement of the Charged-Current Inclusive Electron Neutrino and Electron Antineutrino Cross Section using the MicroBooNE Detector in the NuMI Beam

UoM administered thesis: Phd

  • Authors:
  • Colton Hill

Abstract

Detection of electron neutrino appearance is the process by which modern neutrino experiments seek to answer the open questions in neutrino physics: CP-violation in the neutrino sector, the neutrino mass ordering, and sterile neutrino oscillations. To date, only three measurements of electron neutrino cross sections have been performed. The analysis presented in this thesis describes the extraction of the first charged-current inclusive $\nu_e$ + $\bar{\nu}_e$ cross section on argon. This calculation was performed with data collected by the MicroBooNE liquid argon time projection chamber using neutrinos from the Neutrino Main Injector (NuMI) beam located at the Fermi National Accelerator Laboratory. Extraction of this cross section is enabled by the first fully-automated inclusive selection of $\nu_e$ + $\bar{\nu}_e$ charged-current interactions in a liquid argon time projection chamber, which provided the largest sample of $\nu_e$ + $\bar{\nu}_e$ charged-current interactions ever observed in liquid argon. The selection utilises the signature capabilities of liquid argon, such as distinguishing electron-like and photon-like electromagnetic showers using their charge-deposition profiles (dE/dx). The evaluation of systematic effects influencing the final cross section, including detector simulation and flux simulation uncertainties, which have been applied for the first time to a MicroBooNE analysis using neutrinos from the NuMI beam, is also presented. The total impact of all systematic and statistical effects results in a 38.44\% uncertainty on the flux integrated cross section, giving $\sigma_{\nu_{e}~+~\bar{\nu}_e} = 4.67 \pm 1.01 \textrm{(stat)} \pm 1.48 \textrm{(sys)} \times 10^{-39}$ cm$^{2}$ per nucleon as the final result, which is in agreement with theoretical predictions.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
  • Stefan Soldner-Rembold (Supervisor)
  • Andrzej Szelc (Supervisor)
Award date31 Dec 2019