Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter

Understand

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period).

• These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances.
• Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces.
• We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations.