BibTeX

@article{2305.07442v4,
Author        = {Delaney Farrell and Pierre Baldi and Jordan Ott and Aishik Ghosh and Andrew W. Steiner and Atharva Kavitkar and Lee Lindblom and Daniel Whiteson and Fridolin Weber},
Title         = {Deducing Neutron Star Equation of State from Telescope Spectra with
  Machine-learning-derived Likelihoods},
Eprint        = {2305.07442v4},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.HE},
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
machine-learning-derived 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.},
Year          = {2023},
Month         = {May},
Url           = {http://arxiv.org/abs/2305.07442v4},
File          = {2305.07442v4.pdf}
}