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Abstract Astrophysically motivated population models for binary black hole (BBH) 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 nonparametric model for the joint population-level distributions of BBH 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 in the 30–40M_⊙range, which is consistent with the predictions of isolated stellar binary evolution, simulations for which place the pileup due to pulsational pair-instability supernovae near 50M_⊙or higher. The other subpopulation with effective spins closer to zero shows a feature at 30–40M_⊙and is consistent with BBHs formed dynamically in globular clusters, which are expected to peak around 30M_⊙. 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. 

Abstract The observation of gravitational waves from multiple compact binary coalescences by the LIGO–Virgo–KAGRA detector networks has enabled us to infer the underlying distribution of compact binaries across a wide range of masses, spins, and redshifts. In light of the new features found in the mass spectrum of binary black holes and the uncertainty regarding binary formation models, nonparametric population inference has become increasingly popular. In this work, we develop a data-driven clustering framework that can identify features in the component mass distribution of compact binaries simultaneously with those in the corresponding redshift distribution, from gravitational-wave data in the presence of significant measurement uncertainties, while making very few assumptions about the functional form of these distributions. Our generalized model is capable of inferring correlations among various population properties, such as the redshift evolution of the shape of the mass distribution itself, in contrast to most existing nonparametric inference schemes. We test our model on simulated data and demonstrate the accuracy with which it can reconstruct the underlying distributions of component masses and redshifts. We also reanalyze public LIGO–Virgo–KAGRA data from events in GWTC-3 using our model and compare our results with those from some alternative parametric and nonparametric population inference approaches. Finally, we investigate the potential presence of correlations between mass and redshift in the population of binary black holes in GWTC-3 (those observed by the LIGO–Virgo–KAGRA detector network in their first three observing runs), without making any assumptions about the specific nature of these correlations.