Orateur
Description
Gravitational waves were predicted in 1916 by Albert Einstein through his theory of general relativity. A century later, on 14 September 2015, the first signal was detected by the LIGO-Virgo Collaboration through interferometric measurements of spacetime distortions produced by the coalescence of two compact objects$^{[1]}$.
This new observational window on the Universe has opened the door to a wide range of astrophysical and cosmological studies. In particular, gravitational wave signals can be used to infer cosmological parameters such as the expansion rate of the Universe – the Hubble constant $H_0$. Observations of black hole and neutron star mergers may help resolve the so-called “Hubble tension”$^{[2]}$, a persistent discrepancy between independent measurements of $H_0$ in modern cosmology.
However, this approach relies on accurately localizing the source of the signal, which remains challenging, especially for binary black hole mergers that lack electromagnetic counterparts. In this work, we present the dark siren method$^{[3]}$, a statistical framework that improves the $H_0$ estimation using compact binary coalescences by combining gravitational-wave data with galaxy catalogs.
Compact binary coalescences are particularly valuable because the luminosity distance to the source is directly encoded in the signal. The key missing parameter is the redshift of the source, which is required to infer the expansion rate. The dark siren method uses Bayesian inference with a redshift prior information extracted from galaxy catalogs considered as potential host of gravitational wave sources. This additional information contributes to overcome the intrinsic degeneracy between source masses and redshift, better constraining the values of $H_0$.
[1] LIGO Scientific Collaboration and Virgo Collaboration, GW150914: The Advanced LIGO Detectors in the Era of First Discoveries, 2016
[2] Verde, L., Treu, T. & Riess, A.G. Tensions between the early and late Universe, 2019
[3] Gray, R. et al. Joint cosmological and gravitational-wave population inference using dark sirens and galaxy catalogues, 2023.