Automated discovery of interpretable gravitational-wave population models
This work addresses the challenge of interpreting gravitational-wave data for astrophysicists, offering an incremental improvement by automating model discovery.
The authors tackled the problem of interpreting complex gravitational-wave population models by using symbolic regression to distill flexible models into interpretable analytic expressions, recovering known models and finding a new empirical model that balances accuracy and simplicity.
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.