From Neutron Star Observables to the Equation of State. II. Bayesian Inference of Equation of State Pressures

Carolyn A. Raithel, Feryal Ozel, Dimitrios Psaltis

Research output: Contribution to journalArticlepeer-review

Abstract

One of the key goals of observing neutron stars is to infer the equation of state (EoS) of the cold, ultradense matter in their interiors. We present here a Bayesian statistical method of inferring the pressures at five fixed densities, from a sample of mock neutron star masses and radii. We show that while five polytropic segments are needed for maximum exibility in the absence of any prior knowledge of the EoS, regularizers are also necessary to ensure that simple underlying EoS are not over-parametrized. For ideal data with small measurement uncertainties, we show that the pressure at roughly twice the nuclear saturation density, fisat, can be inferred to within 0.3 dex for many realizations of potential sources of uncertainties. The pressures of more complicated EoS with significant phase transitions can also be inferred to within 30%. We also find that marginalizing the multi-dimensional parameter space of pressure to infer a mass-radius relation can lead to biases of nearly 1 km in radius, towards larger radii. Using the full, five-dimensional posterior likelihoods avoids this bias.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Apr 3 2017

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'From Neutron Star Observables to the Equation of State. II. Bayesian Inference of Equation of State Pressures'. Together they form a unique fingerprint.

Cite this