Description
The DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project will produce thousands of Milky Way-mass, cosmological hydrodynamic zoom-in simulations that simultaneously vary both the baryonic and dark matter physics. The large scale of this dataset presents a unique opportunity to investigate the role of both the light and dark sectors in the formation and evolution of galaxies. However, analysing and providing insight into the structures within the thousands of central haloes is non-trivial, and requires the development of a common analysis framework. Basis function expansions provide a natural solution, as they are a means of producing homogeneously-derived, tailored metrics to define and quantify the structural features of the stellar, gaseous, and dark matter structure(s) in each zoom-in galaxy in DREAMS. In the context of cosmological simulations, basis function expansions have been successfully used in a number of analyses to characterize cosmological dark matter haloes and their response to perturbations, while the disks have generally received less attention. We are remedying this disparity by developing the requisite machinery for basis function expansions conditioned on disk distributions. I will outline this work and present early results of applying this methodology to cosmological stellar disks at redshift z = 0 in both warm dark matter and cold dark matter Universes in DREAMS. This talk will highlight the unique capabilities of basis function expansions in big data and cosmological simulation applications, and will conclude with a discussion of future avenues for exploration.
