Bayesian Galaxy Shape Measurement for Weak Lensing Surveys - III. Application to the Canada-France-Hawaii Telescope Lensing Survey

TL;DR

This paper presents a Bayesian method for measuring weak gravitational lensing shear in galaxy surveys, applied to CFHTLenS, incorporating realistic survey properties and noise bias correction for accurate ellipticity estimation.

Contribution

It introduces a full analysis pipeline based on lensfit that models PSF variations and jointly measures galaxy shapes from multiple exposures, improving shear measurement accuracy.

Findings

Achieved accurate shear measurements on CFHTLenS data.

Developed empirical noise bias correction based on realistic simulations.

Demonstrated the effectiveness of the Bayesian shape measurement approach.

Abstract

A likelihood-based method for measuring weak gravitational lensing shear in deep galaxy surveys is described and applied to the Canada-France-Hawaii Telescope (CFHT) Lensing Survey (CFHTLenS). CFHTLenS comprises 154 sq deg of multicolour optical data from the CFHT Legacy Survey, with lensing measurements being made in the i' band to a depth i'(AB)<24.7, for galaxies with signal-to-noise ratio greater than about 10. The method is based on the lensfit algorithm described in earlier papers, but here we describe a full analysis pipeline that takes into account the properties of real surveys. The method creates pixel-based models of the varying point spread function (PSF) in individual image exposures. It fits PSF-convolved two-component (disk plus bulge) models, to measure the ellipticity of each galaxy, with bayesian marginalisation over model nuisance parameters of galaxy position, size, brightness and bulge fraction. The method allows optimal joint measurement of multiple, dithered image exposures, taking into account imaging distortion and the alignment of the multiple measurements. We discuss the effects of noise bias on the likelihood distribution of galaxy ellipticity. Two sets of image simulations that mirror the observed properties of CFHTLenS have been created, to establish the method's accuracy and to derive an empirical correction for the effects of noise bias.