{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/143302"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/143302","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Deep Learning for the KamLAND-Zen Search for 0𝜈𝛽𝛽","abstract":"Neutrinoless double beta decay (0𝜈𝛽𝛽) is a major interest in neutrino physics. Discovery of 0𝜈𝛽𝛽 would demonstrate that neutrinos are Majorana fermions and that lepton number is not a symmetry of nature, thus providing a possible explanation for the observed matter-antimatter asymmetry of the universe. KamLAND-Zen is a leading search for 0𝜈𝛽𝛽, having placed the most stringent limit on its half-life at [formula] at 90% C.L. in ¹³⁶Xe. The next phase of KamLAND-Zen is currently running and will place even more stringent limits on the half-life. The sensitivity of KamLAND-Zen is primarily limited by backgrounds, including the muon spallation background ¹⁰C. We present a machine learning algorithm based on a convolutional neural network (CNN) that is able to separate ¹⁰C events from 136Xe events in Monte Carlo simulated data. With a typical kiloton-scale detector configuration like the KamLAND-Zen detector, we find that the algorithm is capable of identifying 61.6% of the ¹⁰C at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources.","abstract_html":"Neutrinoless double beta decay (0𝜈𝛽𝛽) is a major interest in neutrino physics. Discovery of 0𝜈𝛽𝛽 would demonstrate that neutrinos are Majorana fermions and that lepton number is not a symmetry of nature, thus providing a possible explanation for the observed matter-antimatter asymmetry of the universe. KamLAND-Zen is a leading search for 0𝜈𝛽𝛽, having placed the most stringent limit on its half-life at [formula] at 90% C.L. in ¹³⁶Xe. The next phase of KamLAND-Zen is currently running and will place even more stringent limits on the half-life. The sensitivity of KamLAND-Zen is primarily limited by backgrounds, including the muon spallation background ¹⁰C. We present a machine learning algorithm based on a convolutional neural network (CNN) that is able to separate ¹⁰C events from 136Xe events in Monte Carlo simulated data. With a typical kiloton-scale detector configuration like the KamLAND-Zen detector, we find that the algorithm is capable of identifying 61.6% of the ¹⁰C at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources.","abstract_has_math":false,"creators":["Fraker, Suzannah"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Physics","school":null,"contributors":[],"advisors":["Winslow, Lindley"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-02","date_published":"2022-02","updated_at":"2026-07-22T22:22:11Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/143302","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Winslow, Lindley"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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Discovery of 0𝜈𝛽𝛽 would demonstrate that neutrinos are Majorana fermions and that lepton number is not a symmetry of nature, thus providing a possible explanation for the observed matter-antimatter asymmetry of the universe. KamLAND-Zen is a leading search for 0𝜈𝛽𝛽, having placed the most stringent limit on its half-life at [formula] at 90% C.L. in ¹³⁶Xe. The next phase of KamLAND-Zen is currently running and will place even more stringent limits on the half-life. The sensitivity of KamLAND-Zen is primarily limited by backgrounds, including the muon spallation background ¹⁰C. We present a machine learning algorithm based on a convolutional neural network (CNN) that is able to separate ¹⁰C events from 136Xe events in Monte Carlo simulated data. With a typical kiloton-scale detector configuration like the KamLAND-Zen detector, we find that the algorithm is capable of identifying 61.6% of the ¹⁰C at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Deep Learning for the KamLAND-Zen Search for 0𝜈𝛽𝛽"]}]}],"canonical_facts":{"dc:contributor.advisor":["Winslow, Lindley"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Physics"],"dc:creator":["Fraker, Suzannah"],"dc:date.accessioned":["2022-06-15T13:11:01Z"],"dc:date.available":["2022-06-15T13:11:01Z"],"dc:date.issued":["2022-02"],"dc:description.abstract":["Neutrinoless double beta decay (0𝜈𝛽𝛽) is a major interest in neutrino physics. Discovery of 0𝜈𝛽𝛽 would demonstrate that neutrinos are Majorana fermions and that lepton number is not a symmetry of nature, thus providing a possible explanation for the observed matter-antimatter asymmetry of the universe. KamLAND-Zen is a leading search for 0𝜈𝛽𝛽, having placed the most stringent limit on its half-life at [formula] at 90% C.L. in ¹³⁶Xe. The next phase of KamLAND-Zen is currently running and will place even more stringent limits on the half-life. The sensitivity of KamLAND-Zen is primarily limited by backgrounds, including the muon spallation background ¹⁰C. We present a machine learning algorithm based on a convolutional neural network (CNN) that is able to separate ¹⁰C events from 136Xe events in Monte Carlo simulated data. With a typical kiloton-scale detector configuration like the KamLAND-Zen detector, we find that the algorithm is capable of identifying 61.6% of the ¹⁰C at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/143302"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Deep Learning for the KamLAND-Zen Search for 0𝜈𝛽𝛽"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Physics"]},"updated_at":"2026-07-22T22:22:11Z"}