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dc.contributor.authorStubbs, Matthew
dc.date.accessioned2022-01-26T23:15:08Z
dc.date.available2022-01-26T23:15:08Z
dc.date.issued2021-11-25
dc.identifier.citationStubbs, Matthew. Using Machine Learning to Improve Neutron Tagging Efficiency in Water Cherenkov Detectors; A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Applied Computer Science, University of Winnipeg. Winnipeg, Manitoba, Canada: University of Winnipeg, 2021. DOI: 10.36939/ir.202201261707.en_US
dc.identifier.urihttps://hdl.handle.net/10680/1985
dc.description.abstractWhen an anti-neutrino collides with a proton in the atomic nucleus, it yields an anti-lepton and a free neutron. In a water Cherenkov neutrino detector like Super-K or the next generation Hyper-K, the free neutron is captured by a hydrogen or gadolinium nucleus about one hundred microseconds after the collision. The low-energy signal from the neutron capture (ranging from 2-8 MeV of gamma rays) is recorded by only tens of photomultiplier tubes (PMTs), making neutron captures difficult to distinguish from radioactive decay, muon spallation and other background sources. Improved methodologies for neutron tagging can advance understanding and enable new research over a survey of topics in particle physics. In this research, machine learning techniques are employed to optimize the neutron capture detection capability in the new intermediate water Cherenkov detector (IWCD) for Hyper-K. In particular, boosting decision tree (XGBoost) and graph neural network (GCN, DGCNN) models are developed and benchmarked against a statistical likelihood-based approach, achieving up to a 10% increase in classification accuracy. Characteristic features are also engineered from the datasets and analyzed using SHAP (SHapley Additive exPlanations) to provide insight into the pivotal factors influencing event type outcomes. Three main datasets were used for evaluative purposes in this research, each consisting of roughly 1.6 million events in total, divided nearly evenly between neutron capture and a distinct background electron source.en_US
dc.description.sponsorshipNSERC, WatchMalen_US
dc.language.isoenen_US
dc.publisherUniversity of Winnipegen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learningen_US
dc.subjectNeutron Taggingen_US
dc.subjectGraph Neural Networks (GNN)en_US
dc.subjectXGBoosten_US
dc.subjectParticle Physicsen_US
dc.titleUsing Machine Learning to Improve Neutron Tagging Efficiency in Water Cherenkov Detectorsen_US
dc.typeThesisen_US
dc.description.degreeMaster of Science in Applied Computer Scienceen_US
dc.publisher.grantorUniversity of Winnipegen_US
dc.identifier.doi10.36939/ir.202201261707en_US
thesis.degree.disciplineApplied Computer Science
thesis.degree.levelmasters
thesis.degree.nameMaster of Science in Applied Computer Science
thesis.degree.grantorUniversity of Winnipeg


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