A simulation study on the constraints of the Hubble constant using sub-threshold GW observation on double neutron star mergers

TL;DR

This study uses simulations of sub-threshold double neutron star mergers to evaluate the potential of gravitational wave observations, combined with electromagnetic counterparts, to measure the Hubble constant and other cosmological parameters.

Contribution

It introduces a simulation framework for sub-threshold GW events and analyzes their impact on Hubble constant estimation, highlighting biases and limitations.

Findings

Measurement error decreases with lower SNR thresholds.

Inferred Hubble constant tends to be biased high at lower thresholds.

Higher order cosmological parameters remain unconstrained.

Abstract

Gravitational waves observation with electromagnetic counterparts provides an approach to measure the Hubble constant which is also known as the bright siren method. Great hope has been put into this method to arbitrate the Hubble tension. In this study, we apply the simulation tool \GWT\, and modeling of the aLIGO-design background to simulate the bright siren catalogues of sub-threshold double neutron star mergers with potential contamination from noise and dis-pairing between gravitational waves and electromagnetic counterparts. The Hubble constant and other cosmology parameters are thus inferred from the simulated catalogues with a Bayesian method. From our simulation study, we reach the following conclusions: 1) the measurement error of the $H_0$ decreases with a lower signal-to-noise ratio threshold (or equivalently the $P_{\rm astro}$) in the region where $P_{\rm astro} \gtrsim $ 0.1, while the inferred most probable $H_0$ trends to bias towards larger values; and 2) other higher order cosmological parameters such as $\Omega_{m}$ remain unconstrained even with the sub-threshold catalogues. We also discuss adding the network of the gravitational wave detectors to the simulation tool and the electromagnetic counterparts follow-up efficiency simulation, which will improve our work in the future.