A Graph-based Neural-network Surrogate Model for an Accelerating Semianalytical Model of Galaxy Formation and Evolution
A neural network that works directly on the branching histories of dark matter halos reproduces the galaxy properties predicted by a full semi-analytic model — stellar mass, luminosity, rotation, gas metal content, and star formation rate — over the first twelve billion years of cosmic time, matching stellar masses to within 0.19 to 0.28 dex. A single such surrogate holds up across different model parameter choices and halo histories, offering a fast stand-in when exploring the parameter space of galaxy formation models.