Inferring neutron star properties through gravitational waves from r-modes and their relativistic counterparts

TL;DR

This paper develops two frameworks to infer neutron star properties from gravitational wave and electromagnetic data, improving parameter estimation accuracy and reducing dependence on electromagnetic distance measurements.

Contribution

It introduces novel methods combining gravitational wave and electromagnetic observations to directly measure neutron star parameters, including distance and internal properties.

Findings

Second framework achieves accurate parameter inference without electromagnetic distance.

Errors in distance measurement dominate in the first framework.

Simulated errors are independent of the neutron star's equation of state.

Abstract

We present two frameworks to infer some of the properties of neutron stars from their electromagnetic radiation and the emission of continuous gravitational waves due to r-modes and their relativistic counterparts, termed axial-led hybrid modes. In the first framework, assuming a distance measurement via electromagnetic observations, we infer three neutron star properties: the moment of inertia, a parameter related to the mode's saturation amplitude, and the component of magnetic dipole moment perpendicular to the rotation axis. Unlike signals from mountains, axial-led hybrid oscillations provide additional information through a parameter ($\kappa$) that satisfies a universal relation with the star's compactness. In the second framework, we utilize this and the relation between the moment of inertia and compactness, in addition to assuming an equation of state and utilizing pulsar frequency measurements, to directly measure the neutron star's distance, along with the parameters above. We employ a Fisher information matrix-based approach for quantitative error estimation in both frameworks. We find that the error in the distance measurement dominates the errors in the first framework for any reasonable observation time. In contrast, the second framework enables accurate parameter inference because it does not depend on electromagnetic distance measurements. Although its applicability is limited to a restricted parameter space and relies on assumptions about the equation of state, the simulated errors in this framework are found to be independent of the equation of state. Finally, we discuss the potential utility and critical limitations of our analyses, and propose possible solutions and directions for future research.