Can We Discover Physical Models Using Machine Learning? A Case Study of Galaxy Sizes

Abstract

We explore the ability of machine learning methods to discover underlying equations of physics by searching for the equations governing galaxy size in a semianalytic model. This case study allows us to evaluate the process as we know the ground truth. We find that we fail to find an equation to predict galaxy size on the entire data set, but are successful when we separate out disk galaxies where we expect the physics driving galaxy size to be different than in bulge-dominated systems. We are also able to find an equation for bulge size, but not without adding an additional feature based on our knowledge of elliptical galaxy scaling relations.

Keywords

• Disk galaxies
• Elliptical galaxies