Searching for Structure in the Binary Black Hole Spin Distribution

Combining gravitational wave observations to infer the astrophysical distribution of black hole spins allows us to determine the relative contribution from different formation scenarios to the population. Many previous works have modeled spin population distributions using strongly parametric models, making strong assumptions about the shape of the underlying distribution. The results obtained with such models are only valid if the allowed shape of the distribution is well-motivated (i.e. for astrophysical reasons). In this work, we relax these prior assumptions and model the spin distributions using a more data-driven approach, modeling these distributions with flexible cubic spline interpolants in order to allow for capturing structures that the strongly parametric models cannot. We find that adding this flexibility to the model substantially increases the uncertainty in the inferred distributions, but find a general trend for lower support at high spin magnitude and a spin tilt distribution consistent with isotropic orientations. Additionally, we find that artifacts from unconverged Monte Carlo integrals in the likelihood can manifest as spurious peaks and structures in inferred distributions, mandating the use of a sufficient number of samples when using Monte Carlo integration for population inference.