Performance of internal Covariance Estimators for Cosmic Shear
  Correlation Functions
  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.
