Learning Galaxy Properties from Merger Trees

Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics. Although semi-analytic models (SAMs) and hydrodynamical simulations have made impressive advances in reproducing galaxy observables across large cosmological volumes, these methods still require significant computation times, representing a barrier to many applications.However, with Machine Learning, simulations and SAMs can now be emulated in seconds.Graph Neural Networks (GNNs) are a powerful class of learning algorithms which can naturally incorporate the very structure of data, and have been shown to perform extremely well on physical modeling, and among the most inherently graph-like structures found in astrophysics are the dark matter merger trees used by SAMs. In this paper we show that several baryonic targets---as predicted by a SAM---can be emulated to unprecedented accuracy using a trained GNN, four orders of magnitude faster than the SAM. The GNN accurately predicts stellar masses for a range of redshifts, and interpolates successfully at redshifts where it was not trained. We compare our results to the current state of the art in the field, and show improvements in reconstruction RMSE of up to a factor of two.