The authors introduce new equation-of-state-insensitive fits and neural-network models for the radius, logarithmic derivative, and effective gravity on rotating neutron star surfaces, with claimed sub-percent test accuracy.
Finding Universal Relations using Statistical Data Analysis
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abstract
We present applications of statistical data analysis methods from both bi- and multivariate statistics to find suitable sets of neutron star features that can be leveraged for accurate and EoS independent -- or universal -- relations. To this end, we investigate the ability of various correlation measures such as Distance Correlation and Mutual Information in identifying universally related pairs of neutron star features. We also evaluate relations produced by methods of multivariate statistics such as Principal Component Analysis to assess their suitability for producing universal relations with multiple independent variables. As part of our analyses, we also put forward multiple entirely novel relations, including a multivariate relation for the $f$-mode frequency of neutron stars with a reduced average relative error of $0.010$, compared to an error of $0.015$ of existing, bivariate relations.
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Universal description of the Neutron Star's surface and its key global properties: A Machine Learning Approach for nonrotating and rapidly rotating stellar models
The authors introduce new equation-of-state-insensitive fits and neural-network models for the radius, logarithmic derivative, and effective gravity on rotating neutron star surfaces, with claimed sub-percent test accuracy.