A non-parametric exploration of binary black hole population properties using gravitational wave observations.

The formation of compact binary systems that merge within a Hubble time remains poorly understood and yet of critical importance to several fields of modern physics. The complex evolutionary phenomena that underlie the various proposed formation scenarios can uniquely characterize the ensemble properties of the resulting compact binaries. Therefore, inferring the population-level distributions of compact binary observables from multiple gravitational wave detections by ground-based interferometers can elucidate the mysterious origins of these systems. However, strong assumptions regarding the functional forms of the inferred distributions can strongly bias the astrophysical conclusions of such investigations. In light of the significant uncertainty regarding known formation models, data-driven population inference methods have thus become increasingly popular. In this presentation, I will introduce GPpop, a non-parametric inference framework based on binned Gaussian processes, that is capable of reconstructing features in the compact binary population while being highly unassuming about the functional form of the underlying distributions being inferred. I will highlight recent developments in GPpop that implement non-parametric explorations of the binary black hole spin distributions as well as their correlations with the corresponding mass and redshift distributions, in the context of both real and simulated data.