Galactification: painting galaxies onto dark matter only simulations using a transformer-based model
This provides an accelerated forward model for cosmology research, enabling rapid generation of galaxy catalogs to match survey volumes, though it is incremental as it builds on existing simulation methods.
The authors tackled the problem of generating realistic mock galaxy catalogs from dark matter simulations by developing a transformer-based model that outputs galaxies with physical properties, demonstrating it reproduces key statistics and captures dependencies on cosmological parameters.
Connecting the formation and evolution of galaxies to the large-scale structure is crucial for interpreting cosmological observations. While hydrodynamical simulations accurately model the correlated properties of galaxies, they are computationally prohibitive to run over volumes that match modern surveys. We address this by developing a framework to rapidly generate mock galaxy catalogs conditioned on inexpensive dark-matter-only simulations. We present a multi-modal, transformer-based model that takes 3D dark matter density and velocity fields as input, and outputs a corresponding point cloud of galaxies with their physical properties. We demonstrate that our trained model faithfully reproduces a variety of galaxy summary statistics and correctly captures their variation with changes in the underlying cosmological and astrophysical parameters, making it the first accelerated forward model to capture all the relevant galaxy properties, their full spatial distribution, and their conditional dependencies in hydrosimulations.