Cosmological inference from standard sirens without redshift measurements

TL;DR

This paper proposes a method to infer cosmological parameters using gravitational wave standard sirens without redshift measurements by leveraging prior redshift distributions, demonstrating promising accuracy with simulated data.

Contribution

It introduces a novel framework for cosmological inference from GW data without redshift, utilizing prior redshift distributions from population models.

Findings

Luminosity distances alone can constrain cosmological parameters effectively.

Approximately 100,000 GW events are needed to measure H0 with 1% precision.

Method achieves about 20% uncertainty in cosmological parameters with simulated data.

Abstract

The purpose of this work is to investigate the prospects of using the future standard siren data without redshift measurements to constrain cosmological parameters. With successful detections of gravitational wave (GW) signals an era of GW astronomy has begun. Unlike the electromagnetic domain, GW signals allow direct measurements of luminosity distances to the sources, while their redshifts remain to be measured by identifying electromagnetic counterparts. This leads to significant technical problems for almost all possible BH-BH systems. It is the major obstacle to cosmological applications of GW standard sirens. In this paper, we introduce the general framework of using luminosity distances alone for cosmological inference. The idea is to use the prior knowledge of the redshift probability distribution for coalescing sources from the intrinsic merger rates assessed with population synthesis codes. Then the posterior probability distributions for cosmological parameters can be calculated. We demonstrate the performance of our method on the simulated mock data and show that the luminosity distance measurement would enable an accurate determination of cosmological parameters up to $20%$ uncertainty level. We also find that in order to infer $H_0$ to 1% level with flat $\Lambda$CDM model, we need about $10^5$ events.