Oral Presentation
Leaving no branches behind: An accurate model for predicting galaxy properties from full sets of merger trees of host dark matter halos
Presenter: Chen-Yu Chuang (ASIAA/NTHU)
Galaxies have played a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs. For such a daunting task, methods that can produce a direct mapping between dark matter halos and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models (SAMs) is way more expansive. Here we present a Graph Neural Network (GNN)-based model that is able to accurately predict key properties of galaxies such as stellar mass, g-r color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history (i.e., the merger trees) of the galaxies. Tests based on the IllustrisTNG300 simulation show that our model can recover the key properties of galaxies to high accuracy, over a wide redshift range (z=0-5), for all galaxies and their progenitors.
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