2020/10/26 by Jarrett Moon, Moon, Jarrett
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Neutrino Physics Research #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2010.14505
openalex publication_date 2020/10/26 · openalex created_date 2024/04/10 · openalex updated_date 2026/07/28
This thesis describes an analysis developed for the MicroBooNE experiment to investigate an anomalous excess of electron-like events observed in the MiniBooNE detector. The hypothesis investigated here is that the MiniBooNE anomaly represents appearance of electron neutrinos. Using an amalgam of novel Deep Learning and standard algorithmic techniques this analysis reconstructs and identifies a highly pure sample of charged current quasi-elastic muon neutrino and electron neutrino interactions. This thesis describes the steps in the analysis chain and provides data-to-simulation comparisons for each step that establish confidence in the final prediction. When interpreted in the context of a νe appearance like model, this analysis predicts a 3.2σ sensitivity to exclude a standard model fluctuation which would appear as a MiniBooNE like anomaly using 7×1020 protons on target of MicroBooNE Data.