Hierarchical Population Inference with Normalizing Flows for Binary Black Holes
Low-dimensional parametric mass models are standard in gravitational-wave population inference, but their rigidity can bias the recovered distribution and the conclusions drawn from it. We present a pipeline in which the source-frame binary-black-hole population in $(z,m_1,m_2)$ is represented by a normalizing flow trained directly through the hierarchical likelihood, with event posterior samples propagating measurement uncertainty and detected injections accounting for selection effects. We restrict population statements to the region supported by the injection campaign; conditioning on this region can itself induce an apparent association between mass and redshift. We therefore introduce a mutual-information diagnostic that compares the reconstruction with a redshift-independent reference under the same support restriction, testing for dependence beyond that induced by the support geometry. We validate the method on two simulated catalogues differing only in whether the characteristic primary-mass scale evolves with redshift. The reconstruction recovers the injected mass structure in both, and the benchmarks establish how the diagnostic behaves in the presence and absence of intrinsic evolution. Applied to the GWTC-5.0 catalogue, the method recovers features near $10\,M_\odot$ and $35\,M_\odot$, consistent with the LVK population analysis of the same catalogue, and finds no evidence for intrinsic evolution of the primary-mass spectrum with redshift. More broadly, the pipeline addresses a problem common to many observational sciences: recovering a population distribution from noisy, indirect, and selection-biased measurements of its members.