Constraining the neutron star equation of state with gravitational wave signals from coalescing binary neutron stars

TL;DR

This study refines methods to constrain neutron star equations of state using gravitational wave signals, highlighting the importance of accurate mass priors and including higher-order tidal effects for reliable inference.

Contribution

It incorporates higher-order tidal effects, neutron star spins, and realistic mass distributions into EOS constraints, revealing increased detection requirements and potential biases.

Findings

Higher-order tidal effects increase the number of detections needed.

Accurate mass priors significantly improve EOS discrimination.

Systematic biases arise from model assumptions and mass distribution uncertainties.

Abstract

Recently exploratory studies were performed on the possibility of constraining the neutron star equation of state (EOS) using signals from coalescing binary neutron stars, or neutron star-black hole systems, as they will be seen in upcoming advanced gravitational wave detectors such as Advanced LIGO and Advanced Virgo. In particular, it was estimated to what extent the combined information from multiple detections would enable one to distinguish between different equations of state through hypothesis ranking or parameter estimation. Under the assumption of zero neutron star spins both in signals and in template waveforms and considering tidal effects to 1 post-Newtonian (1PN) order, it was found that O(20) sources would suffice to distinguish between a hard, moderate, and soft equation of state. Here we revisit these results, this time including neutron star tidal effects to the highest order currently known, termination of gravitational waveforms at the contact frequency, neutron star spins, and the resulting quadrupole-monopole interaction. We also take the masses of neutron stars in simulated sources to be distributed according to a relatively strongly peaked Gaussian, as hinted at by observations, but without assuming that the data analyst will necessarily have accurate knowledge of this distribution for use as a mass prior. We find that especially the effect of the latter is dramatic, necessitating many more detections to distinguish between different EOS and causing systematic biases in parameter estimation, on top of biases due to imperfect understanding of the signal model pointed out in earlier work. This would get mitigated if reliable prior information about the mass distribution could be folded into the analyses.