$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees. (arXiv:2210.13473v1 [astro-ph.GA])

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 cosmologically significant volumes, these methods still require
significant computation times, representing a barrier to many applications.
Graph Neural Networks (GNNs) 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 …

arxiv astro merger trees