Improving astrophysical scaling relations with machine learning

Finding low-scatter relationships in properties of complex systems (e.g., stars, supernovae, galaxies) is important to gain physical insights into them and/or to estimate their distances/masses. As the size of simulation/observational datasets grow, finding low-scatter relationships in the data becomes extremely arduous using manual data analysis methods. I will show how machine learning techniques can be used to expeditiously search for such relations in abstract high-dimensional data-spaces. Focusing on clusters of galaxies, I will present new scaling relations between their properties obtained using ML. These relations can enable more accurate inference of cosmology and baryonic feedback from upcoming surveys of galaxy clusters such as ACT, SO, eROSITA and CMB-S4.