Optimized localization for gravitational-waves from merging binaries

TL;DR

This paper improves the speed of gravitational-wave source localization from merging binaries by optimizing Bayesian priors and signal models, significantly reducing high-latency sky map production time.

Contribution

It introduces optimized Bayesian priors and signal models that decrease the time needed for high-latency localization in gravitational-wave observations.

Findings

Up to 60% reduction in high-latency sky map production time.

Optimization choices improve rapid localization for binary neutron star mergers.

Enhances coordination for multi-messenger astronomy.

Abstract

The Advanced LIGO and Virgo gravitational wave observatories have opened a new window with which to study the inspiral and mergers of binary compact objects. These observations are most powerful when coordinated with multi-messenger observations. This was underlined by the first observation of a binary neutron star merger GW170817, coincident with a short Gamma-ray burst, GRB170817A, and the identification of the host galaxy NGC~4993 from the optical counterpart AT~2017gfo. Finding the fast-fading optical counterpart critically depends on the rapid production of a sky-map based on LIGO/Virgo data. Currently, a rapid initial sky map is produced followed by a more accurate, high-latency, $\gtrsim{12}{\rm{hr}}$ sky map. We study optimization choices of the Bayesian prior and signal model which can be used alongside other approaches such as reduced order quadrature. We find these yield up to a $60\%$ reduction in the time required to produce the high-latency localisation for binary neutron star mergers.