Model-independent inference of the population properties of compact binary coalescences from gravitational wave observations.

The observation of gravitational waves from multiple compact binary coalescences by the LIGO-VIRGO and KAGRA detector networks has enabled us to infer the underlying distribution of compact binaries across a wide range of masses, spins and redshifts. Studying the population of compact binaries through GWTC-3 has facilitated the exploration of binary formation and evolution channels. In light of the new features found in the mass spectrum of binary blackholes 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 redshift evolution of the merger rate from gravitational wave data in the presence of significant measurement uncertainties, while making no assumption on the functional form of these distributions. We test our model on simulated data and demonstrate the accuracy with which it can re-construct the underlying distributions of component masses and redshift. 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 schemes. Lastly, we explore the potential generalization of our analysis to being able to infer the population distribution of additional parameters like component spins simultaneously with those of the existing parameters. In this talk, I will summarize the methodology of our inference framework, present the results obtained from the afore-mentioned analyses and discuss the astrophysical implications of said results.