Performance of internal covariance estimators for cosmic shear correlation functions
- Univ. Observatory Munich, Munich (Germany); Max Planck Institute for Extraterrestrial Physics, Garching (Germany)
- California Institute of Technology, Pasadena, CA (United States)
Data re-sampling methods such as the delete-one jackknife are a common tool for estimating the covariance of large scale structure probes. In this paper we investigate the concepts of internal covariance estimation in the context of cosmic shear two-point statistics. We demonstrate how to use log-normal simulations of the convergence field and the corresponding shear field to carry out realistic tests of internal covariance estimators and find that most estimators such as jackknife or sub-sample covariance can reach a satisfactory compromise between bias and variance of the estimated covariance. In a forecast for the complete, 5-year DES survey we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in the $$\Omega_m$$-$$\sigma_8$$ plane as measured with internally estimated covariance matrices is on average $$\gtrsim 85\%$$ of the volume derived from the true covariance matrix. The uncertainty on the parameter combination $$\Sigma_8 \sim \sigma_8 \Omega_m^{0.5}$$ derived from internally estimated covariances is $$\sim 90\%$$ of the true uncertainty.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1254158
- Report Number(s):
- arXiv:1508.00895; FERMILAB-PUB-16-182-AE; 1386645
- Journal Information:
- Monthly Notices of the Royal Astronomical Society, Vol. 456, Issue 3; ISSN 0035-8711
- Publisher:
- Royal Astronomical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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