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This thesis describes an analysis developed for the MicroBooNE experiment toinvestigate an anomalous excess of electron-like events observed in theMiniBooNE detector. The hypothesis investigated here is that the MiniBooNEanomaly represents appearance of electron neutrinos. Using an amalgam of novelDeep Learning and standard algorithmic techniques this analysis reconstructsand identifies a highly pure sample of charged current quasi-elastic muonneutrino and electron neutrino interactions. This thesis describes the steps inthe analysis chain and provides data-to-simulation comparisons for each stepthat establish confidence in the final prediction. When interpreted in thecontext of a $\nu e$ appearance like model, this analysis predicts a3.2$\sigma$ sensitivity to exclude a standard model fluctuation which wouldappear as a MiniBooNE like anomaly using $7\times10^{20}$ protons on target ofMicroBooNE Data.

Using Deep Learning Techniques to Search for the MiniBooNE Low Energy Excess in MicroBooNE with > 3$\sigma$ Sensitivity