Galaxy clusters are the most massive gravitationally bound structures that can serve as powerful probes of cosmology as well as unique laboratories for astrophysics. However, the relative lack of understanding of the astrophysics of galaxy clusters hinder our ability to infer, to high accuracy, the abundances of galaxy clusters, which are the key quantities for constraining cosmology. In this talk, I will highlight how combining different modeling methods: analytical, numerical, and machine learning, will advance our understanding of galaxy cluster astrophysics, which will help us control uncertainties in cosmological constraints in upcoming galaxy cluster multiwavelength surveys.
Erwin Lau is a currently a visiting scientist at the Smithsonian Astrophysical Observatory. He has previously held positions at Yale University and the University of Miami. He specializes the modeling of galaxy clusters with numerical simulations and analytic methods.