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Trends in the Population of Binary Black Holes Following the Fourth Gravitational-Wave Transient Catalog: a Data-Driven Analysis
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Trends in the Population of Binary Black Holes Following the Fourth Gravitational-Wave Transient Catalog: a Data-Driven Analysis
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Current population models of binary black hole distributions are difficult to interpret because standard population inferences hinge on modeling choices, which can mask or mimic real structure. The maximum population likelihood ``$\pistroke$ formalism'' provides a means to investigate and interpret features in the distribution of binary black holes using only data -- without specifying a population model. It tells us if features inferred from current population models are truly present in the data or if they arise from model misspecification. It also provides guidance for developing new models by highlighting previously unnoticed features. In this study, we utilize the $\pistroke$ formalism to examine the binary black hole population in the LIGO--Virgo--KAGRA (LVK) fourth Gravitational-Wave Transient Catalog (GWTC-4). Our analysis supports the existence of a gap around $45\,M_\odot$ in the secondary black hole mass distribution and identifies a widening in the distribution of the effective inspiral spin parameter $\chi_\text{eff}$ near this mass as recently reported by Tong et al. (2025). Similar to earlier studies, we find support for an anti-correlation between $\chi_\text{eff}$ and mass ratio. However, we argue that this may be a spurious correlation arising from misspecification of the joint distribution of black hole masses. Furthermore, we identify support for dimensionless black hole spin magnitudes at approximately $\chi \approx 0.2$ and $\chi\approx0.7$. The data support the existence of a correlation between the spin magnitudes $\chi_1$ and $\chi_2$, though subsequent study is required to determine if this feature is statistically significant. The accompanying data release includes $\pistroke$ samples, which can be used to compare theoretical predictions to LVK data and to assess assumptions in parameterised models.
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