Reconstructing parametric gravitational-wave population fits from non-parametric results without refitting the data

TL;DR

This paper introduces a method to reconstruct different population models from existing non-parametric gravitational-wave data fits, streamlining analysis and avoiding repeated data processing.

Contribution

It provides a practical recipe to derive parametric population distributions from non-parametric fits without re-accessing raw data or refitting, enhancing efficiency and interpretability.

Findings

Successfully applied to black-hole binary mass distributions

Enables comparison of multiple models using a goodness-of-fit metric

Reduces computational effort by avoiding repeated data refitting

Abstract

Combining multiple events into population analyses is a cornerstone of gravitational-wave astronomy. A critical component of such studies is the assumed population model, which can range from astrophysically motivated functional forms to non-parametric treatments that are flexible but difficult to interpret. In practice, the current approach is to fit the data multiple times with different population models to identify robust features. We propose an alternative strategy: assuming the data have already been fit with a flexible model, we present a practical recipe to reconstruct the population distribution of a different model. As our procedure postprocesses existing results, it avoids the need to access the underlying gravitational-wave data again and handle selection effects. Additionally, our reconstruction metric provides a goodness-of-fit measure to compare multiple models. We apply this method to the mass distribution of black-hole binaries detected by LIGO/Virgo/KAGRA. Our work paves the way for streamlined gravitational-wave population analyses by fitting the data once and for all with advanced non-parametric methods and careful handling of selection effects, while the astrophysical interpretation is then made accessible using our reconstruction procedure on targeted models. The key principle is that of conceptually separating data description from data interpretation.