Large scale structure prior knowledge in the dark siren method

Abstract Gravitational wave dark sirens are a powerful tool for cosmology and inference of compact object population hyperparameters. They allow for a measurement of the luminosity distance to the source, but not their redshift. Galaxy catalogues in the source localization volume can be used to infer the redshift of the source in a statistical manner. Catalogues are, however, limited by their incompleteness, which can be significant at redshifts corresponding to current GW events. In this work, we detail how to implement in practice variance completion, a novel galaxy completion method which uses knowledge of the large scale structure to optimize the potential of dark sirens analyses. We compress the prediction for the missing number of galaxies into a ratio between the predictions of variance completion and the standard homogeneous completion method. This ratio format can be easily incorporated into existing line of sight computations used in dark sirens software; we demonstrate this procedure using the GLADE+ galaxy catalogue and the gwcosmo software package. We discuss the robustness of the method, and apply it to well-localized event GW190814 as a proof of concept. Finally, we apply the method to data from the third observing run of LIGO-Virgo-KAGRA, finding that it yields results that are consistent with homogeneous completion. We also discuss the prospects for an improvement if the GW localization volume shrinks.