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Learning Galaxy Properties from Merger Trees with Mangrove
Efficiently mapping between baryonic properties and 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, both still require significant computation times, and are hard to succinctly analyze, 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 cake talk I will 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 with scatters two times lower than other methods across a simulation box of side length 75 Mpc/h in 40 seconds, 4 orders of magnitude faster than a SAM and 9 orders of magnitude faster than a hydro simulation. I’ll also show how Mangrove allows for quantification of the dependence of galaxy properties on merger history, making it possible to learn about the simulations on a new level. I will also compare Mangrove results to the current state of the art in emulating the dark matter – galaxy connection and show significant improvements for all target properties.
Mangrove is publicly available at https://github.com/astrockragh/Mangrove.
In this talk I will summarise the work achieved so far in my PhD and discuss plans for the rest of the PhD. I will summarise two projects nearing completion that explore the formation and quenching of the first massive galaxies in COSMOS2020. The first, entitled “COSMOS2020: Explore the epoch of quenching at z>3 with a new colour diagram” presents a new colour diagram to find massive quenched galaxies in photometric data, and presents the results of this applied to the latest COSMOS catalog. The second, entitled ” COSMOS2020: Star formation histories of massive quiescent galaxies at 3<z<5 imply early mass assembly followed by rapid quenching” explores the use of non-parametric methods to reconstruct the stellar assembly of the first massive galaxies and answer the questions: when did they form, when did the quench, and possibly – how did they quench? The final part of the talk details my plans for exploring quenching with JWST, specifically using data from the Canadian NIRISS Unbiased Cluster Survey (CANUCS).