{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/329413"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/329413","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Neutrino interaction vertex reconstruction and particle identification in the MicroBooNE detector","abstract":"This thesis presents the results of a study measuring and improving the quality of neutrino interaction vertex reconstruction and particle identification (PID) in the MicroBooNE detector. The detector comprises a liquid argon time-projection chamber (LArTPC) with a light-collection system, permitting precise tracking of neutrino interaction final states. MicroBooNE's primary physics goal is to resolve the low-energy electron neutrino appearance anomalies observed at MiniBooNE and LSND. The experiment therefore requires high-quality neutrino interaction vertex reconstruction and PID, which together strongly influence event reconstruction quality and energy/momentum estimation. Improvements to the vertex reconstruction are made through the development of powerful new variables and the application of machine learning techniques; these algorithms are now the default used at MicroBooNE and have enabled new studies of neutrino interactions with up to six charged particles in the final state. A robust PID method (FOMA) is developed using a novel analytic approximation to the mode of the dE/dx distribution. A deep learning PID method (PidNet) is also proposed, based on convolutional neural networks (CNNs) and a semi-supervised representation learning method. The performance of the two approaches is compared and contrasted with PIDA, the default PID algorithm used at MicroBooNE. 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Improvements to the vertex reconstruction are made through the development of powerful new variables and the application of machine learning techniques; these algorithms are now the default used at MicroBooNE and have enabled new studies of neutrino interactions with up to six charged particles in the final state. A robust PID method (FOMA) is developed using a novel analytic approximation to the mode of the dE/dx distribution. A deep learning PID method (PidNet) is also proposed, based on convolutional neural networks (CNNs) and a semi-supervised representation learning method. The performance of the two approaches is compared and contrasted with PIDA, the default PID algorithm used at MicroBooNE. 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A deep learning PID method (PidNet) is also proposed, based on convolutional neural networks (CNNs) and a semi-supervised representation learning method. The performance of the two approaches is compared and contrasted with PIDA, the default PID algorithm used at MicroBooNE. 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