The MicroBooNE Search For Anomalous Electron Neutrino Appearance Using Image Based Data Reconstruction
By: Victor Genty
Publisher: [publisher not identified]
Published: 2019
Place of publication: [New York, N.Y.?]
Language: eng
Description: This thesis presents the MicroBooNE search for the MiniBooNE low energy excess using a fully automated image based data reconstruction scheme. A suite of traditional and deep learning computer vision algorithms are developed for identification of charge current quasi-elastic (CCQE) like muon and electron neutrino interactions using the MicroBooNE detector. Given a model of the MiniBooNE low energy excess as due to an enhancement of electron neutrino type events, this analysis predicts a combined statistical and systematic 3.8σ low energy signal in 13.2 × 1020 POT of MicroBooNE data. When interpreted in the context of νμ → νe 3 + 1 sterile neutrino oscillations a best fit point of (∆m241, sin2 2θeμ) = (0.063,0.794) is found with a 90% confidence allowed region consistent with > 0.1 eV2 oscillations.
Notes: Department: Physics.

Thesis advisor: Michael Herman Shaevitz.

Thesis (Ph.D.)--Columbia University, 2019.