UC Irvine

The interior of a neutron star is a unique astrophysical laboratory for studying matter at extreme densities and pressures beyond what is replicable in terrestrial experiments. While there is no direct way to simulate the interior of these stars, one promising avenue to learning more about the equation of state (EOS) of such matter is through X-rays emitted from the star's surface. The current state-of-the-art method for inference of EOS from a star's X-ray spectra uses piece-wise, simulation-based likelihoods that rely on theoretical assumptions complicated by systematic uncertainties. To reduce the dimensionality of the problem, this method infers macroscopic properties of the star (mass and radius) from emitted X-ray spectra, and from those quantities infers the EOS. This work approaches the same problem using machine learning techniques, demonstrating a series of enhancements to the current state-of-the-art by realistic uncertainty quantification and reducing the need for theoretical assumptions. We also demonstrate novel inference of the EOS directly from high-dimensional simulated X-ray spectra from neutron stars that negate the need for a piece-wise approach. This inference allows for a natural propagation of uncertainties from the X-ray spectra by conditioning the discussed networks on realistic sources of uncertainty for each star.