Learning the relations between neutron star and nuclear matter properties with symbolic regression
N. K. Patra, Tuhin Malik, Kai Zhou, and Constan\c{c}a Provid\^encia

TL;DR
This study uses symbolic regression and principal component analysis to uncover complex, nonlinear relationships between nuclear matter properties and neutron star characteristics, confirming key correlations and identifying dominant factors.
Contribution
It introduces a novel application of symbolic regression combined with PCA to analyze nuclear matter and neutron star properties, including hyperonic effects, revealing key property relationships.
Findings
Robust correlation between neutron star tidal deformability and pressure at twice saturation density.
Isovector properties contribute approximately 90% to the neutron star radius and deformability.
Hyperon inclusion affects the relationships between nuclear matter properties and neutron star features.
Abstract
The equation of state (EOS) of dense matter in neutron stars (NSs) remains uncertain, particularly at supra-nuclear densities where complex nuclear interactions and the potential presence of exotic matter, like hyperons, come into play. The complex relationships existing between nuclear matter and neutron star properties are investigated. The focus is on their nonlinearities and interdependencies. In our analysis, we apply a machine learning algorithm known as symbolic regression, paired with principal component analysis, to datasets generated from Bayesian inference over relativistic mean-field models. A systematic Principal Component Analysis has allowed to break down the percentage contribution of each element or feature in the relationships obtained. This study examines two main models (datasets): the NL model, which includes nucleonic degrees of freedom; and the NL-hyp model, which…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Nuclear physics research studies · Statistical Mechanics and Entropy
