Year: 2024
Type: article
Abstract: The LIGO-Virgo-KAGRA collaboration has announced the detection to date of almost 100 binary black holes that have been used in several studies to infer the features of the underlying binary black hole population. From these objects 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 characterisation, 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 have implemented this method in an open-access package, popstock, and extensively tested its capabilities. Using popstock, we investigated 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 two-thirds power law at low frequencies is both waveform and mass-model dependent, and that the power-law signal is likely shallower than previously modelled, given the current waveform and population knowledge.
Authors: Arianna I. Renzini, Jacob Golomb
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: The Operation and Maintenance of the Laser Interferometer Gravitational Wave Observatory (LIGO), The Construction of the Advanced Laser Interferometer Gravitational Wave Observatory (AdvLIGO), Gravitational Wave Physics and Astrophysics with LIGO, Stochastic Rewind and fast-forward: calibrating LISA with LIGO's black holes and stochastic background, Gravitational-Wave Data Science with a LIGO at Caltech, G434337789
OpenAlex ID: W6884505397
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