FLAMINGO: Calibrating large cosmological hydrodynamical simulations with machine learning

Understand

To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables.

• In simulations, feedback processes originate on scales that remain unresolved.
• Therefore, they need to be sourced via subgrid models that contain free parameters.
• We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO cosmological hydrodynamical simulations.