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Muon neutrino reconstruction with machine-learning techniques at the ICARUS detector

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dc.contributor.authorMueller, Justin J., author
dc.contributor.authorMooney, Michael, advisor
dc.contributor.authorHarton, John, committee member
dc.contributor.authorBrandl, Alexander, committee member
dc.contributor.authorBrewer, Samuel, committee member
dc.contributor.authorTerao, Kazuhiro, committee member
dc.date.accessioned2024-09-09T20:52:17Z
dc.date.available2024-09-09T20:52:17Z
dc.date.issued2024
dc.description.abstractThe ICARUS T600 LArTPC detector successfully ran for three years at the underground LNGS laboratories, providing a first sensitive search for LSND-like anomalous electron neutrino appearance in the CNGS beam. After a significant overhauling at CERN, the T600 detector has been placed in its experimental hall at Fermilab, fully commissioned, and the first events observed with full detector readout. Regular data-taking began in May 2021 with neutrinos from the Booster Neutrino Beam (BNB) and neutrinos six degrees off-axis from the Neutrinos at the Main Injector (NuMI). Modern developments in machine learning have allowed for the development of an end-to-end machine-learning-based event reconstruction for ICARUS data. This reconstruction folds in 3D voxel-level feature extraction using sparse convolutional neural networks and particle clustering using graph neural networks to produce outputs suitable for physics analyses. The analysis presented in this thesis demonstrates a high-purity and high-efficiency selection of muon neutrino interactions in the BNB suitable for the physics goals of the ICARUS experiment and the Short-Baseline Neutrino Program.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierMueller_colostate_0053A_18584.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239302
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleMuon neutrino reconstruction with machine-learning techniques at the ICARUS detector
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplinePhysics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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