Automated discovery of interpretable gravitational-wave population models

TL;DR

This paper introduces an automated method using symbolic regression to derive interpretable analytic models of gravitational-wave populations from data, balancing accuracy with physical interpretability.

Contribution

It presents a novel approach that combines flexible models with symbolic regression to automatically discover simple, interpretable population models for gravitational-wave data.

Findings

Recovered known GW population models like power-law-plus-Gaussian

Discovered a new empirical population model with high accuracy and simplicity

Demonstrated the method's potential for application to other astrophysical phenomena

Abstract

We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible models such as Gaussian Mixture Models have become more important in fitting the distribution of GW properties due to their expressivity. However, flexible models come with many parameters that lack physical motivation, making interpreting the implication of these models challenging. In this work, we demonstrate symbolic regression can complement flexible models by distilling the posterior predictive distribution of such flexible models into interpretable analytic expressions. We recover common GW population models such as a power-law-plus-Gaussian, and find a new empirical population model which combines accuracy and simplicity. This demonstrates a strategy to automatically discover interpretable population models in the ever-growing GW catalog, which can potentially be applied to other astrophysical phenomena.