Data release: Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes
Authors/Creators
- 1. University of Wisconsin–Milwaukee
Description
The data required to reproduce the analyses of "Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes" (arxiv:2404.03166). The main inference code can be found at https://github.com/AnaryaRay1/gppop/tree/spin-dev (commit: ee5ffc). To reproduce the analyses, follow the instructions at https://github.com/AnaryaRay1/bbh-subpopulations-scripts (commit de88f93). Frozen versions of these repositories that were used to generate all the results are available as part of this data release, in the files "gppop_spin_dev_ee5ffc421.tar.gz" and "bbh-subpopulations-scripts_de88f931.tar.gz" respectively.
Abstract
Astrophysically motivated population models for binary black hole observables are often insufficient to capture the imprints of multiple formation channels. This is mainly due to the strongly parametrized nature of such investigations. Using a non-parametric model for the joint population-level distributions of binary black hole component masses and effective inspiral spins, we find hints of multiple subpopulations in the third gravitational wave transient catalog. The higher (more positive) spin subpopulation is found to have a mass-spectrum without any feature at the $30-40M_{\odot}$, which is consistent with the predictions of isolated stellar binary evolution, simulations for which place the pile up due to pulsational pair-instability supernovae near $50M_{\odot}$ or higher. The other sub-population with effective spins closer to zero shows a feature at $30-40M_{\odot}$ and is consistent with binary black holes formed dynamically in globular clusters, which are expected to peak around $30M_{\odot}$. We also compute merger rates for these two subpopulations and find that they are consistent with the theoretical predictions of the corresponding formation channels. We validate our results by checking their robustness against variations of several model configurations and by analyzing large simulated catalogs with the same model.
Notes
Files
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