Quick recipes for gravitational-wave selection effects

Abstract

Accurate modeling of selection effects is a key ingredient to the success of gravitational-wave astronomy. The detection probability plays a crucial role in both statistical population studies, where it enters the hierarchical Bayesian likelihood, and astrophysical modeling, where it is used to convert predictions from population-synthesis codes into observable distributions. We review the most commonly used approximations, extend them, and present some recipes for a straightforward implementation. These include a closed-form expression capturing both multiple detectors and noise realizations written in terms of the so-called Marcum Q-function and a ready-to-use mapping between signal-to-noise ratio thresholds and false-alarm rates from state-of-the-art detection pipelines. The bias introduced by approximating the matched filter signal-to-noise ratio with the optimal signal-to-noise ratio is not symmetric: sources that are nominally below threshold are more likely to be detected than sources above threshold are to be missed. Using both analytical considerations and software injections in detection pipelines, we confirm that including noise realizations when estimating the selection function introduces an average variation of a few %. This effect is most relevant for large catalogs and specific subpopulations of sources at the edge of detectability (e.g. high redshifts).

16 pages, 5 figures

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• Forecasting the population properties of merging black holes
• Comparing astrophysical models to gravitational-wave data in the observable space
• Mass function of stellar black holes as revealed by the LIGO-Virgo-KAGRA observations
• Reducing systematic uncertainties in gravitational-wave population analyses by increasing the detection threshold
• Reconsidering the consistent use of precessing, higher order multipole models for gravitational wave analyses
• Lightweight posterior construction for gravitational-wave catalogs with the Kolmogorov-Arnold network