Gravitational-wave astronomy requires population-informed parameter estimation

TL;DR

This paper emphasizes that hierarchical population inference is essential for accurate astrophysical interpretation of gravitational-wave data, as it corrects biases from initial unphysical priors.

Contribution

It demonstrates the importance of population-informed parameter estimation for interpreting gravitational-wave observations using the latest LIGO-Virgo-KAGRA data.

Findings

Hierarchical inference reduces biases in source property estimates.

Population-informed parameters are better suited for astrophysical analysis.

Catalog-level analysis improves detection of exceptional events.

Abstract

Gravitational-wave events are interpreted in terms of Bayesian posteriors for their source properties inferred under unphysical reference priors. Though these parameter estimates are important intermediate data products for downstream analyses, across the catalog they provide generically biased sourced properties and are therefore unsuitable for direct astrophysical interpretation. Hierarchical parameter estimation is the solution, where joint analysis of the entire catalog of observations not only reduces statistical uncertainties but actually informs the correct prior. Population-informed source properties from there derived are naturally suited to astrophysical interpretation and catalog statistics, such as identification of exceptional events from previous and ongoing observing runs. Using the latest LIGO-Virgo-KAGRA data, we thus demonstrate that population inference is not optional to interpret gravitational-wave observations.