Implicit correlations within phenomenological parametric models of the neutron star equation of state
Isaac Legred, Katerina Chatziioannou, Reed Essick, Philippe, Landry

TL;DR
This paper compares parametric and nonparametric models of the neutron star equation of state, revealing that parametric models impose strong, often unsupported correlations that can bias inferences about neutron star properties.
Contribution
It provides a systematic comparison of modeling approaches, quantifies limitations of parametric models, and discusses their impact on understanding dense matter physics.
Findings
Parametric models impose strong, opaque correlations between density scales.
Such correlations can lead to biased inferences of the equation of state.
Nonparametric models offer greater flexibility but are more complex.
Abstract
The rapid increase in the number and precision of astrophysical probes of neutron stars in recent years allows for the inference of their equation of state. Observations target different macroscopic properties of neutron stars which vary from star to star, such as mass and radius, but the equation of state allows for a common description of all neutron stars. To connect these observations and infer the properties of dense matter and neutron stars simultaneously, models for the equation of state are introduced. Parametric models rely on carefully engineered functional forms that reproduce a large array of realistic equations of state. Such models benefit from their simplicity but are limited because any finite-parameter model cannot accurately approximate all possible equations of state. Nonparametric models overcome this by increasing model freedom at the cost of increased complexity.…
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