Identifying strongly lensed gravitational wave signals from binary black hole mergers

TL;DR

This paper develops a Bayesian method to identify strongly lensed gravitational wave signals from binary black hole mergers, which appear as multiple magnified images, using simulated data.

Contribution

It introduces a novel Bayesian inference technique specifically designed to detect strongly lensed GW signals among numerous binary black hole events.

Findings

The method effectively distinguishes lensed pairs in simulated GW data.

It demonstrates high accuracy in identifying lensed signals with realistic noise levels.

The approach can be applied to future GW observations to find lensing events.

Abstract

Based on the rate of gravitational-wave (GW) detections by Advanced LIGO and Virgo, we expect these detectors to observe hundreds of binary black hole mergers as they achieve their design sensitivities (within a few years). A small fraction of them can undergo strong gravitational lensing by intervening galaxies, resulting in multiple images of the same signal. To a very good approximation, the lensing magnifies/de-magnifies these GW signals without affecting their frequency profiles. We develop a Bayesian inference technique to identify pairs of strongly lensed images among hundreds of binary black hole events and demonstrate its performance using simulated GW observations.