期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 462, 期 3, 页码 2681-2694出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stw1821
关键词
large-scale structure of Universe
资金
- Templeton Foundation
- U.S. Department of Energy [DE-SC0013718]
- NASA [NNH12ZDA001N- EUCLID]
- NSF [AST1412966]
- DOE [DE-SC0008080]
- Alfred P. Sloan Foundation
- National Science Foundation
- U.S. Department of Energy Office of Science
- University of Arizona
- Brazilian Participation Group
- Brookhaven National Laboratory
- Carnegie Mellon University
- University of Florida
- French Participation Group
- German Participation Group
- Harvard University
- Instituto de Astrofisica de Canarias
- Michigan State/Notre Dame/JINA Participation Group
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- Max Planck Institute for Astrophysics
- Max Planck Institute for Extraterrestrial Physics
- New Mexico State University
- New York University
- Ohio State University
- Pennsylvania State University
- University of Portsmouth
- Princeton University
- Spanish Participation Group
- University of Tokyo
- University of Utah
- Vanderbilt University
- University of Virginia
- University of Washington
- Yale University
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
- U.S. Department of Energy (DOE) [DE-SC0008080] Funding Source: U.S. Department of Energy (DOE)
We introduce a new method for estimating the covariance matrix for the galaxy correlation function in surveys of large-scale structure. Our method combines simple theoretical results with a realistic characterization of the survey to dramatically reduce noise in the covariance matrix. For example, with an investment of only approximate to 1000 CPU hours we can produce a model covariance matrix with noise levels that would otherwise require similar to 35 000 mocks. Non-Gaussian contributions to the model are calibrated against mock catalogues, after which the model covariance is found to be in impressive agreement with the mock covariance matrix. Since calibration of this method requires fewer mocks than brute force approaches, we believe that it could dramatically reduce the number of mocks required to analyse future surveys.
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