Rapid Bayesian position reconstruction for gravitational-wave transients

TL;DR

BAYESTAR is a fast Bayesian algorithm that produces accurate sky localization maps for gravitational-wave sources within seconds, enabling timely electromagnetic follow-up observations.

Contribution

The paper introduces BAYESTAR, a rapid Bayesian localization method that significantly reduces computation time compared to traditional MCMC techniques.

Findings

BAYESTAR produces sky maps in seconds.

Localization accuracy is comparable to full MCMC methods.

Enables prompt electromagnetic follow-up of gravitational-wave events.

Abstract

Within the next few years, Advanced LIGO and Virgo should detect gravitational waves from binary neutron star and neutron star-black hole mergers. These sources are also predicted to power a broad array of electromagnetic transients. Because the electromagnetic signatures can be faint and fade rapidly, observing them hinges on rapidly inferring the sky location from the gravitational-wave observations. Markov chain Monte Carlo methods for gravitational-wave parameter estimation can take hours or more. We introduce BAYESTAR, a rapid, Bayesian, non-Markov chain Monte Carlo sky localization algorithm that takes just seconds to produce probability sky maps that are comparable in accuracy to the full analysis. Prompt localizations from BAYESTAR will make it possible to search electromagnetic counterparts of compact binary mergers.