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Thesis

Search for a low-energy excess of electron neutrinos in MicroBooNE

Abstract:

The Micro Booster Neutrino Experiment (MicroBooNE) is a Liquid Argon Time Projection Chamber (LArTPC) designed for short-baseline neutrino physics at the Fermi National Accelerator Laboratory. The main physics goal of MicroBooNE is to address the low-energy excess of electron-like events observed by the MiniBooNE experiment and, if confirmed, clarify its nature. The MiniBooNE experiment is a Cherenkov detector and this technology does not allow to distinguish between electrons and single photons in the final state. LArTPC detectors, instead, offer excellent granularity and powerful separation between electrons and photons. For this reason, they represent an ideal technology for the detection of electron neutrino interactions. This thesis presents the first fully-automated electron neutrino selection in a LArTPC. The selection looks for charged-current electron neutrino interactions with no pions and at least one proton in the final state. It is applied on a sub-sample of the data acquired by the detector in the Booster Neutrino Beam, corresponding to 4.34 x 1019 protons-on-target. A validation of the analysis is performed on two orthogonal side-bands, enriched with neutral-current and charged-current muon neutrino interactions, respectively. The uncertainties on the neutrino cross sections, flux, and detector simulation are evaluated. The MicroBooNE detector is placed off-axis with the Neutrinos at the Main Injector (NuMI) beam. An independent dataset of events acquired by triggering on the NuMI beam is employed to measure the significance of the detection of electron neutrinos in the beam using the selection presented here. The sensitivity of the MicroBooNE experiment to the MiniBooNE low-energy excess of electron-like events is evaluated. The efficiency and background-rejection power necessary to achieve 5σ sensitivity are also quantified.

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Division:
MPLS
Department:
Physics
Sub department:
Particle Physics
Department:
University of Oxford
Role:
Author

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Role:
Supervisor
Role:
Supervisor


Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford


Language:
English
Keywords:
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UUID:
uuid:b8f89e19-5c7c-433b-81bf-47271d94da9d
Deposit date:
2019-03-21

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