Effective Mass and Machine Learning in Strongly Interacting Neutron Matter

dc.contributor.advisorGezerlis, Alexandros
dc.contributor.authorIsmail, Nawar
dc.date.accessioned2020-09-17T13:59:05Z
dc.date.available2020-09-17T13:59:05Z
dc.date.copyright2020-09
dc.date.created2020-09-09
dc.degree.departmentDepartment of Physicsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmePhysicsen_US
dc.description.abstractThis thesis contains two topics related to strongly interacting neutron matter. The first considers the effective mass, described in Landau Fermi liquid theory, which aims to capture many-body physics in terms of a single parameter. Monte Carlo algorithms were used to determine energies for finite particle simulations. Studying the deviations from the thermodynamic limit for the non-interacting gas allows for claims about the macroscopic scale. Following this systematic investigation, the density dependence was determined to be less than one for the considered densities. The second topic focuses on machine learning algorithms which have proliferated into many fields, with an emerging popularity in nuclear physics. These algorithms work by learning patterns found in datasets to develop their predictive power. After identifying and resolving issues introduced by a small dataset, they were used to extrapolate finite calculations to zero effective-range in the thermodynamic limit, which best approximates neutron matter.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada
dc.identifier.urihttps://hdl.handle.net/10214/21294
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectnuclear physicsen_US
dc.subjectneutron matteren_US
dc.subjectstrongly interactingen_US
dc.subjectfermionsen_US
dc.subjectmachine learningen_US
dc.subjecteffective massen_US
dc.subjectquantum monte carloen_US
dc.subjectQMCen_US
dc.subjectmany-bodyen_US
dc.subjectLandau Fermi Liquid Theoryen_US
dc.subjectunitary gasen_US
dc.subjectchiralen_US
dc.subjectfinite size effectsen_US
dc.subjectthermodynamic limiten_US
dc.subjectauxiliary field diffusion monte carloen_US
dc.subjectneural networken_US
dc.subjectextrapolationen_US
dc.titleEffective Mass and Machine Learning in Strongly Interacting Neutron Matteren_US
dc.typeThesisen_US

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