SIMBAD references

2018MNRAS.476.2220L - Mon. Not. R. Astron. Soc., 476, 2220-2229 (2018/May-2)

Gravitational lensing of gravitational waves: a statistical perspective.

LI S.-S., MAO S., ZHAO Y. and LU Y.

Abstract (from CDS):

In this paper, we study the strong gravitational lensing of gravitational waves (GWs) from a statistical perspective, with particular focus on the high frequency GWs from stellar binary black hole coalescences. These are most promising targets for ground-based detectors such as Advanced Laser Interferometer Gravitational Wave Observatory (aLIGO) and the proposed Einstein Telescope (ET) and can be safely treated under the geometrical optics limit for GW propagation. We perform a thorough calculation of the lensing rate, by taking account of effects caused by the ellipticity of lensing galaxies, lens environments, and magnification bias. We find that in certain GW source rate scenarios, we should be able to observe strongly lensed GW events once per year (∼1 yr–1) in the aLIGO survey at its design sensitivity; for the proposed ET survey, the rate could be as high as ∼80 yr–1. These results depend on the estimate of GW source abundance, and hence can be correspondingly modified with an improvement in our understanding of the merger rate of stellar binary black holes. We also compute the fraction of four-image lens systems in each survey, predicting it to be ∼30 per cent for the aLIGO survey and ∼6 per cent for the ET survey. Finally, we evaluate the possibility of missing some images due to the finite survey duration, by presenting the probability distribution of lensing time delays. We predict that this selection bias will be insignificant in future GW surveys, as most of the lens systems (∼90 per cent) will have time delays less than ∼1 month, which will be far shorter than survey durations.

Abstract Copyright: © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal keyword(s): gravitational lensing: strong - gravitational waves

Simbad objects: 6

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