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oai:dspace.mit.edu:1721.1/1433022022-06-16T03:10:12Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Winslow, Lindley Fraker, Suzannah Massachusetts Institute of Technology. Department of Physics 2022-06-15T13:11:01Z 2022-06-15T13:11:01Z 2022-02 2022-05-25T22:43:34.180Z https://hdl.handle.net/1721.1/143302 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. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Deep Learning for the KamLAND-Zen Search for 0𝜈𝛽𝛽 Thesis application/pdf Master Master of Science in Physics Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="cc31f478-22b7-4b7f-95d7-35125d945ab2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Deep Learning for the KamLAND-Zen Search for 0𝜈𝛽𝛽</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2022-02</PublicationDate> <Authors> <Author> <DisplayName>Fraker, Suzannah</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <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> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>