Deducing Neutron Star Equation of State Parameters Directly From Telescope Spectra with Uncertainty-Aware Machine Learning

Understand

Neutron stars provide a unique laboratory for studying matter at extreme pressures and densities.

• While there is no direct way to explore their interior structure, X-rays emitted from these stars can indirectly provide clues to the equation of state (EOS) of superdense nuclear matter through the inference of the star's mass and radius.
• However, inference of EOS directly from a star's X-ray spectra is extremely challenging and is complicated by systematic uncertainties.
• The current state of the art is to use simulation-based likelihoods in a piece-wise method, which first infer the star's mass and radius to reduce the dimensionality of the problem, and from those quantities infer the EOS.