Theses Doctoral

The MicroBooNE Search For Anomalous Electron Neutrino Appearance Using Image Based Data Reconstruction

Genty, Victor

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


  • thumnail for Genty_columbia_0054D_15198.pdf Genty_columbia_0054D_15198.pdf application/pdf 22.6 MB Download File

More About This Work

Academic Units
Thesis Advisors
Shaevitz, Michael Herman
Ph.D., Columbia University
Published Here
April 26, 2019