  The origins of merging compact binaries observed by gravitational-wave
detectors remains highly uncertain. Several astrophysical channels may
contribute to the overall merger rate, with distinct formation processes
imprinted on the structure and correlations in the underlying distributions of
binary source parameters. In the absence of confident theoretical models, the
current understanding of this population mostly relies on simple parametric
models that make strong assumptions and are prone to misspecification. Recent
work has made progress using more flexible nonparametric models, but detailed
measurement of the multidimensional population remains challenging. In pursuit
of this, we present PixelPop-a high resolution Bayesian nonparametric model to
infer joint distributions and parameter correlations with minimal assumptions.
PixelPop densely bins the joint parameter space and directly infers the merger
rate in each bin, assuming only that bins are coupled to their nearest
neighbors. We demonstrate this method on mock populations with and without
bivariate source correlations, employing several statistical metrics for
information gain and correlation significance to quantify our nonparametric
results. We show that PixelPop correctly recovers the true populations within
posterior uncertainties and offers a conservative assessment of
population-level features and parameter correlations. Its flexibility and
tractability make it a useful data-driven tool to probe gravitational-wave
populations in multiple dimensions.
