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

Abstract

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 identication 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 3.8 low energy signal in 13:2 1020 POT of MicroBooNE data. When interpreted in the context of ! e 3 + 1 sterile neutrino oscillations a best t point of (m2 41; sin2 2e) = (0:063; 0:794) is found with a 90% condence allowed region consistent with > 0:1 eV2 oscillations.

Authors:
ORCiD logo [1]
  1. Columbia U.
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1515053
Report Number(s):
FERMILAB-THESIS-2019-06
1735515
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Thesis/Dissertation
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Genty, Victor. The MicroBooNE Search For Anomalous Electron Neutrino Appearance Using Image Based Data Reconstruction. United States: N. p., 2019. Web. doi:10.2172/1515053.
Genty, Victor. The MicroBooNE Search For Anomalous Electron Neutrino Appearance Using Image Based Data Reconstruction. United States. doi:10.2172/1515053.
Genty, Victor. Tue . "The MicroBooNE Search For Anomalous Electron Neutrino Appearance Using Image Based Data Reconstruction". United States. doi:10.2172/1515053. https://www.osti.gov/servlets/purl/1515053.
@article{osti_1515053,
title = {The MicroBooNE Search For Anomalous Electron Neutrino Appearance Using Image Based Data Reconstruction},
author = {Genty, Victor},
abstractNote = {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 identication 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 3.8 low energy signal in 13:2 1020 POT of MicroBooNE data. When interpreted in the context of ! e 3 + 1 sterile neutrino oscillations a best t point of (m2 41; sin2 2e) = (0:063; 0:794) is found with a 90% condence allowed region consistent with > 0:1 eV2 oscillations.},
doi = {10.2172/1515053},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Thesis/Dissertation:
Other availability
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