Approximating neutron-star radii using gravitational-wave only measurements with symbolic regression
gr-qc, astro-ph.HE, cs.LG
Submitted: 2025-04-28
Updated: 2025-07-09
Comments: 10 pages, 6 figures, 2 tables; PRD accepted
DOI: 10.1103/cv6n-xtsf
Code: https://github.com/mbejger/pysr_r-as-mlambda
License: http://creativecommons.org/licenses/by/4.0/
The gist: Gravitational waves emitted by binary neutron-star inspirals carry information on components' masses and tidal deformabilities, but not directly radii, which are measured by electromagnetic
Terminology
Abstract
Gravitational waves emitted by binary neutron-star inspirals carry information on components' masses and tidal deformabilities, but not directly radii, which are measured by electromagnetic observations of neutron stars. To improve the multi-messenger astronomy studies of neutron stars, an expression for neutron-star radii as a function of gravitational-wave only data would be advantageous, as it would allow to compare information from two different channels. In order to do so, a symbolic regression method, pySR, is trained on TOV solutions to piecewise polytropic EOS input to discover an approximate symbolic expression for the neutron-star radius as a function of gravitational-wave measurements only. The approximation is tested on piecewise polytropic EOS NS data, as well as on NS sequences based on selected realistic (non-polytropic) dense-matter theory EOSs, achieving consistent agreement between the ground truth values and the symbolic approximation for a broad range of NS parameters covering current astrophysical observations, with average radii differences of few hundred meters. Additionally, the approximation is applied to the GW170817 gravitational-wave mass and tidal deformability posteriors, and compared to reported inferred radius distributions.
Sources
- GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
- Advanced LIGO
- Advanced Virgo: a 2nd generation interferometric gravitational wave detector
- Multi-messenger Observations of a Binary Neutron Star Merger
- The Radius of PSR J0740+6620 from NICER and XMM-Newton Data
- Tidal deformability from GW170817 as a direct probe of the neutron star radius
- Neutron star tidal deformability and equation of state constraints
- I-Love-Q Relations in Neutron Stars and their Applications to Astrophysics, Gravitational Waves and Fundamental Physics
- Measuring the neutron star tidal deformability with equation-of-state-independent relations and gravitational waves
- Inferring neutron star properties from GW170817 with universal relations
- Neural networks reconstruction of the dense-matter equation of state from neutron-star parameters
- Deducing Neutron Star Equation of State from Telescope Spectra with Machine-learning-derived Likelihoods
- Insights into neutron star equation of state by machine learning
- Enhancing Gravitational-Wave Science with Machine Learning
- Machine Learning Applications in Gravitational Wave Astronomy
- Applications of machine learning in gravitational wave research with current interferometric detectors
- Automated discovery of interpretable gravitational-wave population models
- Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes
- Inferring the Equation of State from Neutron Star Observables via Machine Learning
- Calibrating global behaviour of equation of state by combining nuclear and astrophysics inputs in a machine learning approach
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