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

Two-dimensional power-law relationships discovered empirically in observed or simulated data are used for inferring properties of a wide variety of astrophysical objects (e.g., stars, supernovae, and galaxies). More accurate relations, which are nonlinear, or contain three or more variables, could easily have been overlooked, as they are difficult to find with manual data-analysis methods. We show that machine learning tools can expeditiously search for such relations in high-dimensional astrophysical data-spaces. In particular, we find improvements to previous relations which have been widely used for estimating masses of clusters of galaxies. Numerous upcoming observational surveys will target galaxy clusters, and our work enables their use to more accurately infer the fundamental properties of the Universe.