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

Abstract

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. We demonstrate a series of enhancements to the state of the art, in terms of realistic uncertainty quantification and improved regression of physical properties with machine learning. We also demonstrate novel inference of the EOS directly from the high-dimensional spectra of observed stars, avoiding the intermediate mass-radius step. Our network is conditioned on the sources of uncertainty of each star, allowing for natural and complete propagation of uncertainties to the EOS.

24 pages, 20 figures

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• Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra
• Deep learning inference of the neutron star equation of state
• Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep learning
• Nuclear mass table in density functional approach inspired by neutron-star observations
• Extracting latent representations from X-ray spectra. Classification, regression, and accretion signatures of Chandra sources