The bahamas project: calibrated hydrodynamical simulations for large-scale structure cosmology

• Published in: Monthly notices of the Royal Astronomical Society 2017-03, Vol.465 (3), p.2936-2936
• Main Authors: McCarthy, Ian G, Schaye, Joop, Bird, Simeon, Le Brun, Amandine MC
• Format: Article
• Language: English
• Subjects: Astronomy; Astrophysics; Baryons; Black holes; Calibration; Computer simulation; Cosmology; Dark energy; Dark matter; Feedback; Galaxies; Mathematical models; Physics; Star & galaxy formation
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stw2792

The evolution of the large-scale distribution of matter is sensitive to a variety of fundamental parameters that characterize the dark matter, dark energy, and other aspects of our cosmological framework. Since the majority of the mass density is in the form of dark matter that cannot be directly observed, to do cosmology with large-scale structure, one must use observable (baryonic) quantities that trace the underlying matter distribution in a (hopefully) predictable way. However, recent numerical studies have demonstrated that the mapping between observable and total mass, as well as the total mass itself, are sensitive to unresolved feedback processes associated with galaxy formation, motivating explicit calibration of the feedback efficiencies. Here, we construct a new suite of large-volume cosmological hydrodynamical simulations (called bahamas, for BAryons and HAloes of MAssive Systems), where subgrid models of stellar and active galactic nucleus feedback have been calibrated to reproduce the present-day galaxy stellar mass function and the hot gas mass fractions of groups and clusters in order to ensure the effects of feedback on the overall matter distribution are broadly correct. We show that the calibrated simulations reproduce an unprecedentedly wide range of properties of massive systems, including the various observed mappings between galaxies, hot gas, total mass, and black holes, and represent a significant advance in our ability to mitigate the primary systematic uncertainty in most present large-scale structure tests.