The Buzzard Simulations

The Buzzard simulations (De Rose et al. 2019) are a series of mock galaxy catalogs designed to be compared against large-volume galaxy surveys and makes use of a method called Addgals that combines machine-learning with more traditional empirical modeling to quickly populate the simulation with galaxies (Wechsler et al. 2022). These mock catalogs recieved numerous methodlogical improvements in To et al. (2024). While these changes improved model output in many ways, in particular it fixed long-standing problems with Buzzard’s galaxy clusters.

The Cardinal webside contains more information and results summaries. Earlier sites for Buzzard include a simulation summary page and this datalab page. The datalab page contains some public data for the Buzzard simulations, but generally data will be made available upon reasonable request to the authors.