Reconstruction of the neutron star equation of state from $w$-quasinormal modes spectra with a piecewise polytropic meshing and refinement method
Juan Mena-Fern\'andez, Luis Manuel Gonz\'alez-Romero

TL;DR
This paper introduces a novel piecewise polytropic meshing and refinement method to reconstruct the neutron star equation of state from quasinormal mode spectra, enabling accurate predictions of physical parameters and constraints from gravitational wave data.
Contribution
The paper presents a new inverse method combining quasinormal modes and piecewise polytropic parametrization for neutron star EoS reconstruction, improving accuracy and efficiency.
Findings
Reconstructed EoS closely matches original with less than 2.5% difference in physical parameters.
Method effectively predicts tidal deformability and rotation parameters.
Constraints from GW170817 are incorporated into the reconstruction process.
Abstract
In this paper we present a new approach to the inverse problem for relativistic stars using quasinormal modes and the piecewise polytropic parametrization of the equation of state. The algorithm is a piecewise polytropic meshing and refinement method that reconstructs the neutron star equation of state from experimental data of the mass and the -quasinormal modes. We present an algorithm able to numerically calculate axial quasinormal modes of neutron stars in an efficient way. We use an initial mesh of equations of state in a -volume of piecewise polytropic parameters that contains most of the candidate equations of state used today. The refinement process drives us to the reconstruction of the equation of state with a certain precision. Using the reconstructed equation of state, we calculate predictions for tidal deformability and slow rotation parameters (moment of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
