A physically modelled selection function for compact binary mergers in the LIGO-Virgo O3 run and beyond

TL;DR

This paper introduces a new physically motivated model for the detection sensitivity of gravitational-wave searches for compact binary coalescences, improving population analysis accuracy by analytically modeling detection probability based on source parameters.

Contribution

The authors develop a smooth, analytic detection probability model for GW searches that replaces importance sampling of injections, enabling more accurate population reconstructions.

Findings

Model accurately matches injection results

Enhances population analysis accuracy

Applied to reconstruct BBH merger rate dependence on redshift

Abstract

Despite the observation of nearly 100 compact binary coalescence (CBC) events up to the end of the Advanced gravitational-wave (GW) detectors' third observing run (O3), there remain fundamental open questions regarding their astrophysical formation mechanisms and environments. Population analysis should yield insights into these questions, but requires careful control of uncertainties and biases. GW observations have a strong selection bias: this is due first to the dependence of the signal amplitude on the source's (intrinsic and extrinsic) parameters, and second to the complicated nature of detector noise and of current detection methods. In this work, we introduce a new physically-motivated model of the sensitivity of GW searches for CBC events, aimed at enhancing the accuracy and efficiency of population reconstructions. In contrast to current methods which rely on re-weighting simulated signals (injections) via importance sampling, we model the probability of detection of binary black hole (BBH) mergers as a smooth, analytic function of source masses, orbit-aligned spins, and distance, fitted to accurately match injection results. The estimate can thus be used for population models whose signal distribution over parameter space differs significantly from the injection distribution. Our method has already been used in population studies such as reconstructing the BBH merger rate dependence on redshift.