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...
Xuejie Li, Zhongxu Zhai, Xiaohu Yang et al.
· The Astrophysical Journal Su... · 0 citations