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

TL;DR

This paper introduces a machine learning approach that directly infers neutron star equation of state parameters from X-ray spectra, incorporating uncertainty quantification and bypassing traditional intermediate steps.

Contribution

It presents a novel machine learning method that directly estimates EOS parameters from spectra with uncertainty awareness, improving over existing indirect inference techniques.

Findings

Enhanced uncertainty quantification in EOS inference.

Successful direct inference of EOS from spectra.

Improved regression accuracy for physical properties.

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.