Bayesian Inverse Problem of Rotating Neutron Stars

TL;DR

This paper develops a Bayesian framework to infer neutron star properties from co- and counter-rotating f-mode frequencies, enabling more accurate stellar parameter reconstruction with potential for future observational applications.

Contribution

It introduces a novel Bayesian inverse problem approach linking neutron star oscillation modes with their internal structure, incorporating realistic equations of state and universal scaling relations.

Findings

Future f-mode observations can significantly improve neutron star parameter estimates.

Two approaches, EOS-dependent and universal scaling, offer complementary advantages.

Informed priors on mass or rotation enhance the robustness of the inference.

Abstract

In this work we provide a framework that connects the co-rotating and counter rotating $f$-mode frequencies of rotating neutron stars with their stellar structure. The accurate computation of these modes for realistic equations of state has been presented recently and they are here used as input for a Bayesian analysis of the inverse problem. This allows to quantitatively reconstruct basic neutron star parameters, such as the mass, radius, rotation rate or universal scaling parameters. We find that future observations of both $f$-mode frequencies, in combination with a Bayesian analysis, would provide a promising direction to solve the inverse stellar problem. We provide two complementary approaches, one that is equation of state dependent and one that only uses universal scaling relations. We discuss advantages and disadvantages of each approach, such as possible bias and robustness. The focus is on astrophysically motivated scenarios in which informed prior information on the neutron star mass or rotation rate can be provided and study how they impact the results.