Testing PSF Interpolation In Weak Lensing With Real Data
  Reconstruction of the point spread function (PSF) is a critical process in
weak lensing measurement. We develop a real-data based and galaxy-oriented
pipeline to compare the performances of various PSF reconstruction schemes.
Making use of a large amount of the CFHTLenS data, the performances of three
classes of interpolating schemes - polynomial, Kriging, and Shepard - are
evaluated. We find that polynomial interpolations with optimal orders and
domains perform the best. We quantify the effect of the residual PSF
reconstruction error on shear recovery in terms of the multiplicative and
additive biases, and their spatial correlations using the shear measurement
method of Zhang et al. (2015). We find that the impact of PSF reconstruction
uncertainty on the shear-shear correlation can be significantly reduced by
cross correlating the shear estimators from different exposures. It takes only
0.2 stars (SNR > 100) per square arcmin on each exposure to reach the best
performance of PSF interpolation, a requirement that is generally satisfied in
the CFHTlenS data.
