$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees

TL;DR

Mangrove is a graph neural network framework that rapidly and accurately predicts galaxy properties from dark matter merger trees, significantly outperforming existing methods in speed and accuracy.

Contribution

Introduces Mangrove, a novel GNN-based emulator that efficiently predicts galaxy properties from merger trees with lower errors and faster computation than previous models.

Findings

Achieves up to two times lower RMS error in galaxy property predictions.

Operates 4 orders of magnitude faster than semi-analytic models.

Enables analysis of galaxy property dependence on merger history.

Abstract

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 of dark matter halos. In this paper we introduce a new, graph-based emulator framework, $\texttt{Mangrove}$, 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 a SAM -- with root mean squared error up to two times lower than other methods across a $(75 Mpc/h)^3$ simulation box in 40 seconds, 4 orders of magnitude faster than the SAM. We show that $\texttt{Mangrove}$ 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. $\texttt{Mangrove}$ is publicly available.