{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/180223"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/180223","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier","abstract":"The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of $\\Delta m^2_{32}$ and $\\theta_{23}$ and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74$\\times10^{20}$ POT, 14 kt equivalent) to provide new best-fit measurements of $\\sin^2(\\theta_{23}) = 0.43$ (with a statistically-degenerate compliment near 0.60) and $|\\Delta m^2_{32}| = 2.48\\times10^{-3}~\\text{eV}^2$.","abstract_html":"The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of <span class=\"etd-inline-math\">\\Delta m<sup>2</sup><sub>32</sub></span> and <span class=\"etd-inline-math\">&theta;<sub>23</sub></span> and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74<span class=\"etd-inline-math\">\\times10<sup>20</sup></span> POT, 14 kt equivalent) to provide new best-fit measurements of <span class=\"etd-inline-math\">\\sin<sup>2</sup>(&theta;<sub>23</sub>) = 0.43</span> (with a statistically-degenerate compliment near 0.60) and <span class=\"etd-inline-math\">|\\Delta m<sup>2</sup><sub>32</sub>| = 2.48\\times10<sup>-3</sup>~\\text{eV}<sup>2</sup></span>.","abstract_has_math":true,"creators":["Rocco, Dominick"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-03","date_published":"2016-03","updated_at":"2026-07-24T05:20:09Z","subjects":["Convolutional Neural Network","Deep Learning","Neutrino","Neutrino Oscillation"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11299/180223","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Rocco, Dominick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-05-12T19:41:11Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-05-12T19:41:11Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-03"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convolutional Neural Network","Deep Learning","Neutrino","Neutrino Oscillation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11299/180223"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["University of Minnesota Ph.D. dissertation. 2016. Major: Physics. Advisor: Gregory Pawloski. 1 computer file (PDF); 242 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of $\\Delta m^2_{32}$ and $\\theta_{23}$ and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74$\\times10^{20}$ POT, 14 kt equivalent) to provide new best-fit measurements of $\\sin^2(\\theta_{23}) = 0.43$ (with a statistically-degenerate compliment near 0.60) and $|\\Delta m^2_{32}| = 2.48\\times10^{-3}~\\text{eV}^2$."]},{"key":"dc:title","label":"Title","values":["Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier"]}]}],"canonical_facts":{"dc:creator":["Rocco, Dominick"],"dc:date.accessioned":["2016-05-12T19:41:11Z"],"dc:date.available":["2016-05-12T19:41:11Z"],"dc:date.issued":["2016-03"],"dc:description":["University of Minnesota Ph.D. dissertation. 2016. Major: Physics. Advisor: Gregory Pawloski. 1 computer file (PDF); 242 pages."],"dc:description.abstract":["The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of $\\Delta m^2_{32}$ and $\\theta_{23}$ and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74$\\times10^{20}$ POT, 14 kt equivalent) to provide new best-fit measurements of $\\sin^2(\\theta_{23}) = 0.43$ (with a statistically-degenerate compliment near 0.60) and $|\\Delta m^2_{32}| = 2.48\\times10^{-3}~\\text{eV}^2$."],"dc:identifier.uri":["http://hdl.handle.net/11299/180223"],"dc:language.iso":["en"],"dc:subject":["Convolutional Neural Network","Deep Learning","Neutrino","Neutrino Oscillation"],"dc:title":["Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:20:09Z"}