• DOI:10.1086/508646
• Corpus ID: 8035934

Breaking Cosmological Degeneracies in Galaxy Cluster Surveys with a Physical Model of Cluster Structure

@article{Younger2006BreakingCD,
  title={Breaking Cosmological Degeneracies in Galaxy Cluster Surveys with a Physical Model of Cluster Structure},
  author={Joshua Daniel Younger and Zolt{\'a}n Haiman and Greg L. Bryan and Sheng Wang Columbia Astrophysics Lab Department of Astronomy and Columbia University Department of Physics and Columbia University Brookhaven National Laboratory},
  journal={The Astrophysical Journal},
  year={2006},
  volume={653},
  pages={27-42}
}

• J. Younger, Z. Haiman, +3 authors Columbia University Brookhaven National Laboratory
• Published 8 May 2006
• Physics
• The Astrophysical Journal

It has been shown that in an idealized galaxy cluster survey, containing � 10,000 clusters, statistical errors on dark energy and other cosmological parameters will be at the percent level. Furthermore, through “self–calibration”, parameters describing the mass–observable relation and cosmology can be simultaneously determined, though at a loss in accuracy by about an order of magnitude. Here we examine an alternative approach to self–calibration, in which a parametrized ab–initio physical model is used to compute theoretical mass–observable relations from the cluster structure. As an example, we use a modified–entropy (“preheating”) model of the intracluster medium, with the history and magnitude of entropy injection as unknown input parameters. Using a Fisher matrix approach, we evaluate the expected simultaneous statistical errors on cosmological and cluster model parameters. We find that compared to a phenomenological parametrization of the mass–observable relation, our physical model yields significantly tighter constraints in both surveys, and offers substantially improved synergy when the two surveys are combined. In a mock X–ray survey, we find statistical errors on the dark energy equation of state are a factor of two tighter than the phenomenological model , with