Deducing neutron star equation of state from telescope spectra with machinelearningderived likelihoods
Abstract
The interiors of neutron stars reach densities and temperatures beyond the limits of terrestrial experiments, providing vital laboratories for probing nuclear physics. While the star's interior is not directly observable, its pressure and density determine the star's macroscopic structure which affects the spectra observed in telescopes. The relationship between the observations and the internal state is complex and partially intractable, presenting difficulties for inference. Previous work has focused on the regression from stellar spectra of parameters describing the internal state. We demonstrate a calculation of the full likelihood of the internal state parameters given observations, accomplished by replacing intractable elements with machine learning models trained on samples of simulated stars. Our machinelearningderived likelihood allows us to perform maximum a posteriori estimation of the parameters of interest, as well as full scans. We demonstrate the technique by inferring stellar mass and radius from an individual stellar spectrum, as well as equation of state parameters from a set of spectra. Our results are more precise than pure regression models, reducing the width of the parameter residuals by 11.8% in the most realistic scenario. The neural networks will be released as a tool for fast simulation of neutron star properties and observed spectra.
 Publication:

Journal of Cosmology and Astroparticle Physics
 Pub Date:
 December 2023
 DOI:
 10.1088/14757516/2023/12/022
 arXiv:
 arXiv:2305.07442
 Bibcode:
 2023JCAP...12..022F
 Keywords:

 Machine learning;
 neutron stars;
 Xrays;
 Bayesian reasoning;
 Astrophysics  High Energy Astrophysical Phenomena;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 18 pages, 8 figures