Trust the process: mapping data-driven reconstructions to informed models using stochastic processes

Abstract Gravitational-wave astronomy has entered a regime where it can extract information about the population properties of the observed binary black holes. The steep increase in the number of detections will offer deeper insights, but it will also significantly raise the computational cost of testing multiple models. To address this challenge, we propose a procedure that first performs a non-parametric ( data-driven ) reconstruction of the underlying distribution, and then remaps these results onto a posterior for the parameters of a parametric ( informed ) model. Doing so, instead of performing a full hierarchical analysis per available model, the non-parametric reconstruction can be efficiently remapped onto posterior distributions for each different model. In addition to yielding the posterior distribution of the model parameters, this method also provides a measure of the model's goodness-of-fit, opening for a new quantitative comparison across models.