Identifying Muon Neutrino Charged-Current Interactions in the MicroBooNE Detector

Abstract

The MicroBooNE experiment is a short-baseline neutrino experiment at the Fermi National Accelerator Laboratory (FNAL) in the US, receiving a highly pure muon neutrino beam from the Booster Neutrino Beam (BNB) and taking data since October 2015. The main physics goal of MicroBooNE is to clarify the nature of the low-energy excess of electron-like events observed by the MiniBooNE Cherenkov detector, which due to its detector technology is unable to resolve whether the observed excess shower-like events are due to electrons or photons. Instead, MicrooBooNE employs cutting-edge liquid argon time projection chamber (LArTPC) technology, which offers excellent spatial and calorimetric resolution, which makes it possible to reconstruct complex neutrino interactions and to efficiently distinguish different final state particle types. This thesis presents a fully-automated event selection of interactions of the types $\nu_{\mu} + \text{Ar} \rightarrow \mu^- + X$ and $\nu_{\mu} + \text{Ar} \rightarrow \mu^- + p + X$ developed using the Pandora multi-algorithm approach to pattern recognition for LArTPC experiments. The analysis in this work is performed on a subset of the MicroBooNE Run 1 dataset corresponding to $4.52 \times 10^{19}$ protons on target acquired by the MicroBooNE detector between February and October 2016. The analysis quantifies the main signal and background interaction channels that produce a reconstructed neutrino interaction with one and two particles in the final state, and quantifies the extent to which different aspects of the Pandora reconstruction affect the selection performance. MicroBooNE is a surface detector, and therefore cosmic-ray tracks are the main observed background. A direction fitting procedure has been implemented in this analysis to formulate directional probabilities, using the Bethe equation and the high-resolution calorimetric information of MicroBooNE, to reduce this cosmic-ray background. This functionality is used to enhance the efficiency of the Pandora cosmic ray tagging logic, and the impact of this procedure on the reconstruction and event selection performance is analysed. The result of this study is two fully-automated selections of $\nu_{\mu} + \text{Ar} \rightarrow \mu^- + X$ (57.91\% pure) and $\nu_{\mu} + \text{Ar} \rightarrow \mu^- + p + X$ (78.30\% pure) with efficiencies of 64.35\% and 62.65\%, respectively.