On parametrised cold dense matter equation of state inference

TL;DR

This paper evaluates Bayesian methods for inferring the equation of state of cold dense matter in compact stars, advocating for a direct posterior estimation approach over indirect methods due to issues with prior definitions and model assumptions.

Contribution

It compares two Bayesian inference paradigms for dense matter equations of state, highlighting the advantages of direct posterior estimation and proposing an alternative to using archival posterior constraints.

Findings

Direct posterior estimation is more tractable and principled.

Indirect parameter estimation can be ill-defined and problematic.

Using piecewise-polytropic models may violate probability transformation conditions.

Abstract

Constraining the equation of state of cold dense matter in compact stars is a major science goal for observing programmes being conducted using X-ray, radio, and gravitational wave telescopes. We discuss Bayesian hierarchical inference of parametrised dense matter equations of state. In particular we generalise and examine two inference paradigms from the literature: (i) direct posterior equation of state 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 large-scale analyses, but is principled and flexible from a Bayesian perspective whilst 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 ill-defined approach to the problem of utilising 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 piecewise-polytropic equation of state 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.