Properties of the binary neutron star merger GW170817

TLDR: In this article, the authors improved initial estimates of the binary's properties, including component masses, spins, and tidal parameters, using the known source location, improved modeling, and recalibrated Virgo data.

Abstract: On August 17, 2017, the Advanced LIGO and Advanced Virgo gravitational-wave detectors observed a low-mass compact binary inspiral. The initial sky localization of the source of the gravitational-wave signal, GW170817, allowed electromagnetic observatories to identify NGC 4993 as the host galaxy. In this work, we improve initial estimates of the binary's properties, including component masses, spins, and tidal parameters, using the known source location, improved modeling, and recalibrated Virgo data. We extend the range of gravitational-wave frequencies considered down to 23 Hz, compared to 30 Hz in the initial analysis. We also compare results inferred using several signal models, which are more accurate and incorporate additional physical effects as compared to the initial analysis. We improve the localization of the gravitational-wave source to a 90% credible region of $16~\mathrm{deg}^2$. We find tighter constraints on the masses, spins, and tidal parameters, and continue to find no evidence for nonzero component spins. The component masses are inferred to lie between 1.00 and 1.89 $M_\odot$ when allowing for large component spins, and to lie between 1.16 and 1.60 $M_\odot$ (with a total mass $2.73^{+0.04}_{-0.01} \, M_\odot$) when the spins are restricted to be within the range observed in Galactic binary neutron stars. Under minimal assumptions about the nature of the compact objects, our constraints for the tidal deformability parameter $\tilde \Lambda$ are $(0,630)$ when we allow for large component spins, and $300^{+420}_{-230}$ (using a 90% highest posterior density interval) when restricting the magnitude of the component spins, ruling out several equation-of-state models at the 90% credible level. Finally, with LIGO and GEO600 data, we use a Bayesian analysis to place upper limits on the amplitude and spectral energy density of a possible post-merger signal. (Abridged)