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Strong-lensing cosmography using third-generation gravitational-wave detectors
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We present a detailed exposition of a statistical method for estimating cosmological parameters from the observation of a large number of strongly lensed binary-black-hole (BBH) mergers observable by next (third) generation (XG) gravitational-wave (GW) detectors. This method, first presented in Jana (2023 Phys. Rev. Lett. 130 261401), compares the observed number of strongly lensed GW events and their time delay distribution (between lensed images) with observed events to infer cosmological parameters. We show that the precision of the estimation of the cosmological parameters does not have a strong dependance on the assumed BBH redshift distribution model. Using the large number of unlensed mergers, XG detectors are expected to measure the BBH redshift distribution with sufficient precision for the cosmological inference. However, a biased inference of the BBH redshift distribution will bias the estimation of cosmological parameters. An incorrect model for the distribution of lens properties can also lead to a biased cosmological inference. However, Bayesian model selection can assist in selecting the right model from a set of available parametric models for the lens distribution. We also present a way to incorporate the effect of contamination in the data due to the limited efficiency of lensing identification methods, so that it will not bias the cosmological inference.
Forward citations
Cited by 2 Pith papers
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Forecasting Constraints on Cosmology and Modified Gravitational-wave Propagation by Combining Strongly Lensed Gravitational Waves and Galaxy Surveys
Simulated doubly lensed gravitational-wave events matched to galaxy surveys give a forecasted H0 precision of 0.42% with next-generation detectors.
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A $\chi^2$ statistic for the identification of strongly lensed gravitational waves from compact binary coalescences
A chi-squared test built from gravitational wave search templates can separate lensed event pairs from unlensed pairs with accuracy comparable to slower Bayesian methods.
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