Estimation of the Hubble parameter from unedited compact object merger catalogues

TL;DR

This paper introduces a new framework for estimating the Hubble parameter using unedited compact object merger catalogs, enabling population inference directly from detection-level data without relying on individual candidate parameter estimation.

Contribution

It presents a novel method that bypasses per-candidate parameter estimation, allowing the use of all candidates, including marginal ones, for cosmological inference from gravitational wave data.

Findings

Effective extraction of cosmological information from marginal candidates

Framework compatible with detection pipelines without additional selection cuts

Potential to improve constraints on the Hubble parameter

Abstract

In recent years, constraints on the Hubble parameter using multiple dark sirens have been made,relying on a galaxy catalogue, correlations between the mass and redshift distributions, or both. Those studies have typically used only significant gravitational wave candidates. In this work, we present a framework for cosmological inference that bypasses per-candidate parameter estimation, uses only detection-level information. This allows the population inference from a candidate list produced directly by a search pipeline, without additional selection cuts. Our method is particularly suited to extracting information from marginal candidates, which are essential for probing the distant universe.