On parametrized cold dense matter equationofstate inference
Abstract
Constraining the equation of state of cold dense matter in compact stars is a major science goal for observing programmes being conducted using Xray, radio, and gravitational wave telescopes. We discuss Bayesian hierarchical inference of parametrized dense matter equations of state. In particular, we generalize and examine two inference paradigms from the literature: (i) direct posterior equationofstate parameter estimation, conditioned on observations of a set of rotating compact stars; and (ii) indirect parameter estimation, via transformation of an intermediary joint posterior distribution of exterior spacetime parameters (such as gravitational masses and coordinate equatorial radii). We conclude that the former paradigm is not only tractable for largescale analyses, but is principled and flexible from a Bayesian perspective while the latter paradigm is not. The thematic problem of Bayesian prior definition emerges as the crux of the difference between these paradigms. The second paradigm should in general only be considered as an illdefined approach to the problem of utilizing archival posterior constraints on exterior spacetime parameters; we advocate for an alternative approach whereby such information is repurposed as an approximative likelihood function. We also discuss why conditioning on a piecewisepolytropic equationofstate model  currently standard in the field of dense matter study  can easily violate conditions required for transformation of a probability density distribution between spaces of exterior (spacetime) and interior (source matter) parameters.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 July 2018
 DOI:
 10.1093/mnras/sty1051
 arXiv:
 arXiv:1804.09085
 Bibcode:
 2018MNRAS.478.1093R
 Keywords:

 dense matter;
 equation of state;
 stars: neutron;
 Astrophysics  High Energy Astrophysical Phenomena;
 General Relativity and Quantum Cosmology;
 Nuclear Theory
 EPrint:
 Accepted for publication in MNRAS, 43 pages, 7 figures