Projections of the uncertainty on the compact binary population background using popstock

TL;DR

This paper introduces popstock, a flexible tool for rapidly estimating the gravitational-wave background spectrum from binary black holes, accounting for population uncertainties, and finds that the background may deviate from the traditional 2/3 power-law.

Contribution

It presents a new, adaptable method and open-source package for modeling the GW background spectrum considering population uncertainties.

Findings

The background signal's power-law slope may be shallower than 2/3.

Uncertainties in population features significantly affect detection forecasts.

The method enables quick exploration of different population models.

Abstract

The LIGO-Virgo-KAGRA collaboration has announced the detection of almost 100 binary black holes so far, which have been used in several studies to infer the features of the underlying binary black hole population. From these, it is possible to predict the overall gravitational-wave (GW) fractional energy density contributed by black holes throughout the Universe, and thus estimate the gravitational-wave background (GWB) spectrum emitted in the current GW detector band. These predictions are fundamental in our forecasts for background detection and characterization, with both present and future instruments. The uncertainties in the inferred population strongly impact the predicted energy spectrum, and in this paper we present a new, flexible method to quickly calculate the energy spectrum for varying black hole population features such as the mass spectrum and redshift distribution. We implement this method in an open-access package, popstock, and extensively test its capabilities. Using popstock, we investigate how uncertainties in these distributions impact our detection capabilities and present several caveats for background estimation. In particular, we find that the standard assumption that the background signal follows a 2/3 power-law at low frequencies is both waveform and mass-model dependent, and that the signal power-law is likely shallower than previously modelled, given the current waveform and population knowledge.