In this dissertation, we introduce a new approach in building a hybrid nuclear model that combines some existing theoretical
models and a \universal" approximator. The goal of such an approach is to obtain new predictions of nuclear masses and charge radii. We
begin our study by investigating nuclear masses based on theoretical and experimental values. Nuclear masses are essential for
astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of the
existing ``state-of-the-art" mass models, a renement is generated based on a Bayesian Neural Network (BNN) formalism. A novel BNN approach
is applied with the aim of optimizing mass residuals between theory and experiment. A signicant improvement (of about 40%) in the mass
predictions of existing models is obtained after BNN renement. Moreover, these improved results are accompanied by proper statistical
errors. By constructing a \world average" of these predictions, we obtained a unied mass model that is used to predict the composition of
the outer crust of a neutron star. In order to get a better description of nuclear structure, a similar procedure is also implemented in
the nuclear charge radius. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge
radii. In turn, these predictions are rened through the BNN approach to generate predictions for the charge radii of thousands of nuclei
throughout the nuclear chart. The neural networks function is trained using charge radii residuals between theoretical predictions and
experimental data. Although the predictions obtained with density functional theory provide a fairly good description of the experiment,
our results show signicant improvement (better than 40%) after BNN renement. Despite the improvement and robust predictions, we failed to
uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain. Overall, we have
successfully demonstrated the ability of the BNN approach to signicantly increase the accuracy of nuclear models in the predictions of
nuclear masses and charge radii. Extension to other nuclear observables is a natural next step in asserting the eectiveness of the BNN
method in nuclear physics. / A Dissertation submitted to the Department of Physics in partial fulfillment of the requirements for
the degree of Doctor of Philosophy. / Fall Semester 2016. / November 9, 2016. / Bayesian Neural Network, Neutron Star, Nuclear Structure / Includes bibliographical references. / Jorge Piekarewicz, Professor Directing Dissertation; Washington Mio, University Representative;
Harrison Prosper, Committee Member; Simon Capstick, Committee Member; Volker Cred´e, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_405639 |
| Contributors | Utama, Raditya (authoraut), Piekarewicz, Jorge, 1956- (professor directing dissertation), Mio, Washington (university representative), Prosper, Harrison B. (committee member), Capstick, Simon, 1958- (committee member), Crede, Volker (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Physics (degree granting departmentdgg) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| Format | 1 online resource (71 pages), computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
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