Testing PSF Interpolation In Weak Lensing With Real Data

TL;DR

This paper evaluates various PSF interpolation methods using real CFHTLenS data to determine their impact on weak lensing shear measurements, highlighting polynomial interpolation as the most effective approach.

Contribution

It introduces a galaxy-oriented pipeline for comparing PSF reconstruction schemes and assesses their influence on shear bias and correlation in real data.

Findings

Polynomial interpolation with optimal parameters performs best.

Cross-correlating shear estimators reduces PSF uncertainty impact.

Only 0.2 stars per square arcmin are needed for optimal PSF interpolation.

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.