A Graph-based Neural-network Surrogate Model for an Accelerating Semianalytical Model of Galaxy Formation and Evolution
Abstract Understanding how galaxy populations emerge and evolve from the growth of dark-matter structures is a central challenge in galaxy formation theory. Semianalytic models (SAMs) provide an efficient framework to address this problem, but exploring large ensembles of merger trees across broad parameter spaces rema...