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Effective Mass and Machine Learning in Strongly Interacting Neutron Matter

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dc.contributor.advisor Gezerlis, Alexandros Ismail, Nawar 2020-09-17T13:59:05Z 2020-09-17T13:59:05Z 2020-09 2020-09-09
dc.description.abstract This 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.sponsorship NSERC en_US
dc.language.iso en en_US
dc.rights Attribution-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject nuclear physics en_US
dc.subject neutron matter en_US
dc.subject strongly interacting en_US
dc.subject fermions en_US
dc.subject machine learning en_US
dc.subject effective mass en_US
dc.subject quantum monte carlo en_US
dc.subject QMC en_US
dc.subject many-body en_US
dc.subject Landau Fermi Liquid Theory en_US
dc.subject unitary gas en_US
dc.subject chiral en_US
dc.subject finite size effects en_US
dc.subject thermodynamic limit en_US
dc.subject auxiliary field diffusion monte carlo en_US
dc.subject neural network en_US
dc.subject extrapolation en_US
dc.title Effective Mass and Machine Learning in Strongly Interacting Neutron Matter en_US
dc.type Thesis en_US Physics en_US Master of Science en_US Department of Physics en_US
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Attribution-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NoDerivatives 4.0 International