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,
non-parametric 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 on 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 non-parametric inference schemes. We test our model
on simulated data and demonstrate the accuracy with which it can re-construct
the underlying distributions of component masses and redshifts. We also
re-analyze public LIGO-Virgo-KAGRA data from events in GWTC-3 using our model
and compare our results with those from some alternative parametric and
non-parametric 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 3 observing runs), without making any
assumptions about the specific nature of these correlations.Comment: Upload accepted versio