Constraining Neutron Star Eos Using Machine Learning and Advanced Nuclear Models
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Physics and Astronomy
Date of Award
Spring 5-1-2026
Abstract
A significant amount of new observational data on neutron stars (NS) has recently been obtained through missions such as NICER and LIGO-Virgo. However, these obser- vations have been insufficient to fully determine the internal structure of the NS through their equation of state (EoS), i.e., the relation between their internal pressure and density. Many EoS have been developed using numerous phenomenological and microscopic nuclear models. The different assumptions and approaches underlying these models lead to an unconstrained view of the NS EoS. To further constrain and find the most suitable EoS, Bayesian analysis techniques have been increasingly used. This method has been under- taken by many authors to probe the impact of phenomenological and microscopic EoS on known data, including recent observations from NICER and LIGO. This thesis builds on previous work that applied Bayesian analysis techniques to constrain the EoS. The linear regression and Bayesian power-law regression model are applied to describe a large number of EoS compiled from the CompOSE database. Additionally, the use of a Bayesian neural network (BNN) is explored. The mean ϵ–P curve from the BNN is then used to solve the Tolman-Oppenheimer-Volkoff Equation. The resulting mass–radius curves that can then be compared with NS mass observations to test validity.
Advisor
Carlos Bertulani
Subject Categories
Physical Sciences and Mathematics | Physics
Recommended Citation
Baker, Josiah, "Constraining Neutron Star Eos Using Machine Learning and Advanced Nuclear Models" (2026). Electronic Theses & Dissertations. 1345.
https://lair.etamu.edu/etd/1345
