Bayesian Model Averaging of Nuclear Mass Models

dc.contributor.authorLundquist, Sebastian
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysiksv
dc.contributor.examinerEkström, Andreas
dc.contributor.supervisorEkström, Andreas
dc.date.accessioned2020-06-29T07:18:05Z
dc.date.available2020-06-29T07:18:05Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractIn this thesis we investigate the performance of Bayesian inference and Bayesian model averaging (BMA) applied to two nuclear mass models, the Duflo-Zuker 10 parameter model (DZ10) and the semi-empirical mass formula (SEMF). The DZ10 and SEMF models both have theoretically and experimentally motivated terms but the relative importance of them is less clear. Using Bayesian inference and BMA we have attempted to quantify model uncertainties and improve inference about nuclear masses. To explore the robustness of our BMA analysis we compare the results using different choices of parameter priors, and vary the assumed model discrepancy. The main focus is on the DZ10 model. We employ the Atomic Mass Evaluation from 1983 (AME83) for parameter estimation, then we evaluate the predictive power of the model using the Atomic Mass Evaluation from 2016 (AME16). In an attempt to determine the limits of stability of visible matter we also make a prediction for the neutron drip lines, in the tin isotopic chain (Z=50) using the DZ10 model trained on AME16. The 1-neutron drip line is predicted to neutron number N=123 [95, 125], and the 2-neutron drip line at N=115 [103, 125]. Where the error bar corresponds to a 68% degree of belief.sv
dc.identifier.coursecodeTIFX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301063
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectBayesian Model Averagingsv
dc.subjectDuflo-Zuker 10sv
dc.subjectnuclear mass modelssv
dc.subjectneutron drip linesv
dc.titleBayesian Model Averaging of Nuclear Mass Modelssv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmePhysics and astronomy (MPPAS), MSc
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