Mangrove: Learning Galaxy Properties from Merger Trees

Abstract

Abstract Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics. Although semianalytic models (SAMs) and hydrodynamical simulations have made impressive advances in reproducing galaxy observables across cosmologically significant volumes, these methods still require significant computation times, representing a barrier to many applications. Graph neural networks have recently proven to be the natural choice for learning physical relations. Among the most inherently graph-like structures found in astrophysics are the dark matter merger trees that encode the evolution of dark matter halos. In this paper, we introduce a new, graph-based emulator framework, Mangrove, and show that it emulates the galactic stellar mass, cold gas mass and metallicity, instantaneous and time-averaged star formation rate, and black hole mass—as predicted by an SAM—with an rms error up to 2 times lower than other methods across a (75 Mpc/h)3 simulation box in 40 s, 4 orders of magnitude faster than the SAM. We show that Mangrove allows for quantification of the dependence of galaxy properties on merger history. We compare our results to the current state of the art in the field and show significant improvements for all target properties. Mangrove is publicly available: https://github.com/astrockragh/Mangrove.