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Friedrich, O.; Seitz, S.; Eifler, T. F. and Gruen, D. (2016): Performance of internal covariance estimators for cosmic shear correlation functions. In: Monthly Notices of the Royal Astronomical Society, Vol. 456, No. 3: pp. 2662-2680

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Data re-sampling methods such as delete-one jackknife, bootstrap or the sub-sample covariance are common tools for estimating the covariance of large-scale structure probes. We investigate different implementations of these methods in the context of cosmic shear two-point statistics. Using lognormal simulations of the convergence field and the corresponding shear field we generate mock catalogues of a known and realistic covariance. For a survey of similar to 5000 deg(2) we find that jackknife, if implemented by deleting sub-volumes of galaxies, provides the most reliable covariance estimates. Bootstrap, in the common implementation of drawing sub-volumes of galaxies, strongly overestimates the statistical uncertainties. In a forecast for the complete 5-yr Dark Energy 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 greater than or similar to 85 per cent of the volume derived from the true covariance matrix. The uncertainty on the parameter combination Sigma(8) similar to sigma(8) Omega(0.5)(m) derived from internally estimated covariances is similar to 90 per cent of the true uncertainty.

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