The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate muons and pions for use in increasing the acceptance rate of muons below the implemented 75cm track length cut in the Charged Current Inclusive (CC-Inclusive) event selection for the CC-Inclusive Cross-Section Measurement. In doing this, we increase acceptance rate for CC-Inclusive events below a specific momentum range.
","abstract_html":"<p>The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate muons and pions for use in increasing the acceptance rate of muons below the implemented 75cm track length cut in the Charged Current Inclusive (CC-Inclusive) event selection for the CC-Inclusive Cross-Section Measurement. In doing this, we increase acceptance rate for CC-Inclusive events below a specific momentum range.</p>","abstract_has_math":false,"creators":["Esquivel, Jessica Nicole"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Mitchell Soderberg"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-13T07:00:00Z","date_published":"2018-05-13T07:00:00Z","updated_at":"2026-07-24T04:55:27Z","subjects":["Charged Current Inclusive","Convolutional Neural Networks","Cross Section, MicroBooNE","Muon Neutrino","Physical Sciences and Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/etd/845","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mitchell Soderberg"]},{"key":"dc:creator","label":"Author","values":["Esquivel, Jessica Nicole"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Physics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Charged Current Inclusive","Convolutional Neural Networks","Cross Section, MicroBooNE","Muon Neutrino","Physical Sciences and Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/etd/845"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate muons and pions for use in increasing the acceptance rate of muons below the implemented 75cm track length cut in the Charged Current Inclusive (CC-Inclusive) event selection for the CC-Inclusive Cross-Section Measurement. In doing this, we increase acceptance rate for CC-Inclusive events below a specific momentum range.
"]},{"key":"dc:title","label":"Title","values":["Muon/Pion separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement."]}]}],"canonical_facts":{"dc:contributor":["Mitchell Soderberg"],"dc:creator":["Esquivel, Jessica Nicole"],"dc:description.abstract":["The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate muons and pions for use in increasing the acceptance rate of muons below the implemented 75cm track length cut in the Charged Current Inclusive (CC-Inclusive) event selection for the CC-Inclusive Cross-Section Measurement. In doing this, we increase acceptance rate for CC-Inclusive events below a specific momentum range.
"],"dc:identifier":["https://surface.syr.edu/etd/845"],"dc:subject":["Charged Current Inclusive","Convolutional Neural Networks","Cross Section, MicroBooNE","Muon Neutrino","Physical Sciences and Mathematics"],"dc:title":["Muon/Pion separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement."],"thesis:degree_discipline":["Physics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:55:27Z"}