Testing PSF Interpolation In Weak Lensing With Real Data
Abstract
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.
Cite
@article{arxiv.1610.09828,
title = {Testing PSF Interpolation In Weak Lensing With Real Data},
author = {Tianhuan Lu and Jun Zhang and Fuyu Dong and Yingke Li and Dezi Liu and Liping Fu and Guoliang Li and Zuhui Fan},
journal= {arXiv preprint arXiv:1610.09828},
year = {2017}
}
Comments
13 pages, 15 figures