Powerful searches for galaxy clusters, the largest bound cosmic structures, are underway using pan-chromatic detection methods. These surveys can identify all of the most massive clusters in the Universe, which will then be mined for clues to dark energy. However, accurate conclusions require understanding the astrophysics that limits systematic uncertainties. This is a computational project to improve understanding of clusters as multi-component, dynamically active systems. State-of-the-art simulations with a range of astrophysical treatments will characterize how the intrinsic, visible content of clusters relates to total mass and epoch. Studying multiple characteristics simultaneously will provide the first estimates of covariance between important, independent measures, such as X-ray temperature and luminosity, optical richness, and total gas thermal energy. Mock sky maps will be used to test and improve cluster detection methods. The studies will be of general use for all cluster surveys, and will include measuring the statistical form of property covariance, understanding the causes and magnitudes of projected source confusion, and testing the effects of merger-driven transients.
This project will train students in analysis involving data-intensive, high performance computing. The lead researcher continues to be active in organizing workshops and conferences, especially those that emphasize young researchers in the field, and intends a spring 2009 workshop to bring together observers, computational modelers and theorists to develop a consensus view of clusters as an astrophysical population, paving the way for dark energy studies.
