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
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-4vyn-je61 |
| Date | January 2019 |
| Creators | Genty, Victor |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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