Automated discovery of interpretable gravitational-wave population models

We present an approach to automatically discoveranalytic population models for gravitational-wave(GW) events from data. As more gravitational-wave (GW) events are detected, flexible modelssuch as Gaussian Mixture Models have becomemore important in fitting the distribution of GWproperties due to their expressivity. However, flex-ible models come with a cost: a large number ofparameters that lack physical motivation, mak-ing interpreting the implication of these modelsvery difficult. In this work, we demonstrate theuse of symbolic regression to distill such flexi-ble models into interpretable analytic expressions.We recover common GW population models suchas a power-law-plus-Gaussian, and find a newempirical population model which combines ac-curacy and simplicity. Our example shows us-ing flexible models together with symbolic re-gression is a promising pathway to automaticallydiscover physically-insightful descriptions of theever-growing GW catalog