Speaker
Description
The observation of galaxies and gravitational waves (GW) emitted by dark sirens provides two different and complementary measures of distance, respectively redshift and luminosity distance. Under the assumption that both dark sirens and galaxies trace the distribution of matter up to some bias parameters, it is possible to infer cosmological parameters by cross-correlating their density maps.
As the number of resolved GW sources is growing with rapidly advancing technologies, we estimate the population of dark sirens that will be detected by future observations such as Ligo-Virgo-Kagra, the Einstein Telescope and the Cosmic Explorer. We compute the cross-correlation between mock data from dark sirens and galaxy redshift surveys. We fit the cross-correlation angular power spectrum by running Markov Chain Monte Carlo (MCMC) with an innovative likelihood. Our results highlight the potential of this method to provide new and independent constrains on cosmological parameters like H_0.
