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

Creators: Renzini, Arianna I.^{1, 2}; Golomb, Jacob³

1. University of Milano-Bicocca
2. INFN Sezione di Milano Bicocca
3. California Institute of Technology

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.

Copyright and License

Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Acknowledgement

We thank Patrick Meyers and Alan Weinstein for invaluable discussions and insight. We thank Thomas Callister for providing the population samples in Callister (2024), and for carefully reading our work. We thank Nicholas Loutrel for consulting on waveform models and their features. AIR is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101064542, and acknowledges support from the NSF award PHY-1912594. JG is supported by NSF award No. 2207758. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation.

Funding

AIR is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101064542, and acknowledges support from the NSF award PHY-1912594. JG is supported by NSF award No. 2207758. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation.

Files

aa51374-24.pdf

Files (3.2 MB)

Name	Size	Download all
aa51374-24.pdf md5:ec365ee86d8092621dab8d55be34b4ae	3.2 MB	Preview Download

Additional details

	All versions	This version
Views	0	0
Downloads	0	0
Data volume	0 Bytes	0 Bytes