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Stochastic gravitational-wave background as a tool to investigate multi-channel astrophysical and primordial black-hole mergers

13 Sep 2021arXiv:2109.05836links table onlyarchive 2025-07-28

Simone S. Bavera, Gabriele Franciolini, Giulia Cusin, Antonio Riotto, Michael Zevin, Tassos Fragos

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The formation of merging binary black holes can occur through multiple astrophysical channels such as, e.g., isolated binary evolution and dynamical formation or, alternatively, have a primordial origin. Increasingly large gravitational-wave catalogs of binary black-hole mergers have allowed for the first model selection studies between different theoretical predictions to constrain some of their model uncertainties and branching ratios. In this work, we show how one could add an additional and independent constraint to model selection by using the stochastic gravitational-wave background. In contrast to model selection analyses that have discriminating power only up to the gravitational-wave detector horizons (currently at redshifts z≲1 for LIGO-Virgo), the stochastic gravitational-wave background accounts for the redshift integration of all gravitational-wave signals in the Universe. As a working example, we consider the branching ratio results from a model selection study that includes potential contribution from astrophysical and primordial channels. We renormalize the relative contribution of each channel to the detected event rate to compute the total stochastic gravitational-wave background energy density. The predicted amplitude lies below the current observational upper limits of GWTC-2 by LIGO-Virgo, indicating that the results of the model selection analysis are not ruled out by current background limits. Furthermore, given the set of population models and inferred branching ratios, we find that, even though the predicted background will not be detectable by current generation gravitational-wave detectors, it will be accessible by third-generation detectors such as the Einstein Telescope and space-based detectors such as LISA.

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