Bayesian and Principal Component Analyses of Neutron Star Properties
N. K. Patra

TL;DR
This paper employs Bayesian methods and Principal Component Analysis to explore how nuclear matter parameters influence neutron star properties, revealing correlations and dependencies that enhance understanding of neutron star matter in a nearly model-independent way.
Contribution
It introduces a comprehensive Bayesian framework combined with PCA to analyze correlations between nuclear matter parameters and neutron star properties, offering new insights into NS matter behavior.
Findings
Identified significant correlations between pressure and NS properties.
Demonstrated the robustness of these correlations across parameter distributions.
Highlighted the importance of multivariate analysis in NS research.
Abstract
A Bayesian method is used in this extensive work to generate a large set of minimally constrained equations of state (EOSs) for matters in neutron stars (NS). These EOSs are analyzed for their correlations with key NS properties, such as the tidal deformability, radius, and maximum mass, within the mass range of . The observed connections between the pressure of -equilibrated matter and the properties of neutron stars at different densities offer significant insights into the behavior of NS matter in a nearly model-independent manner. The study also examines the influence of various factors on the correlation of symmetry energy parameters, such as slope and curvature parameters at saturation density () with the tidal deformability and radius of neutron stars. This study investigates the robustness of the observed correlations by…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Geophysics and Gravity Measurements
