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

TL;DR

This paper introduces a two-step method combining non-parametric data reconstruction with parametric model remapping to efficiently analyze gravitational-wave data, enabling better model comparison and reduced computational costs.

Contribution

It presents a novel approach that separates data-driven reconstruction from model fitting, improving efficiency and providing a quantitative goodness-of-fit measure.

Findings

The method reduces computational costs compared to traditional approaches.

It allows for effective model comparison using goodness-of-fit metrics.

The approach is applicable to large datasets in gravitational-wave astronomy.

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. The computational cost is primarily absorbed by the initial non-parametric step, while the remapping procedure is both significantly easier to perform and computationally cheaper. 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.