Gravitational wave distance estimation using intrinsic signal properties: dark sirens as distance indicators

TL;DR

This paper introduces a simple, GW signal-based model for estimating distances to binary black hole mergers, providing a quick alternative to complex Bayesian methods without relying on electromagnetic counterparts.

Contribution

It presents a novel analytical approach using intrinsic GW properties for distance estimation, independent of EM data and detailed waveform modeling.

Findings

The model agrees well with Bayesian distance estimates for 87 GW events.

It offers a computationally efficient preliminary distance estimation method.

Graphical comparisons show consistent performance across multiple events.

Abstract

Gravitational Waves (GWs) provide a powerful means for cosmological distance estimation, circumventing the systematic uncertainties associated with traditional electromagnetic (EM) indicators. This work presents a model for estimating distances to binary black hole (BBH) mergers using only GW data, independent of EM counterparts or galaxy catalogs. By utilizing the intrinsic properties of the GW signal, specifically the strain amplitude and merger frequency, our model offers a computationally efficient preliminary distance estimation approach that could complements existing Bayesian parameter estimation pipelines. In this work, we examine a simplified analytical expression for the GW luminosity distance derived from General Relativity (GR), based on the leading-order quadrupole approximation. Without incorporating post-Newtonian (PN) or numerical relativity (NR) corrections, or modeling spin, eccentricity, or inclination, we test how closely this expression can reproduce distances reported by full Bayesian inference pipelines. We apply our model to 87 events from the LIGO-Virgo-Kagra (LVK) Gravitational Wave Transient Catalogues (GWTC), computing distances for these sources. Our results demonstrate consistent agreement with GWTC-reported distances, further supported by graphical comparisons that highlight the model's performance across multiple events.