Calibration of colour gradient bias in shear measurement using HST/CANDELS data

TL;DR

This study quantifies the colour gradient bias in shear measurements caused by PSF chromaticity in space-based observations, demonstrating it can be accurately measured and corrected using HST data for future weak lensing surveys.

Contribution

The paper introduces a method to quantify and correct colour gradient bias in shear measurements using multi-colour HST data, relevant for Euclid and similar surveys.

Findings

CG bias correlates with galaxy colour but weakly with redshift.

Biases are generally below 1%, manageable with morphological info.

Noise in HST data has negligible impact on bias measurement.

Abstract

Accurate shape measurements are essential to infer cosmological parameters from large area weak gravitational lensing studies. The compact diffraction-limited point-spread function (PSF) in space-based observations is greatly beneficial, but its chromaticity for a broad band observation can lead to new subtle effects that could hitherto be ignored: the PSF of a galaxy is no longer uniquely defined and spatial variations in the colours of galaxies result in biases in the inferred lensing signal. Taking Euclid as a reference, we show that this colourgradient bias (CG bias) can be quantified with high accuracy using available multi-colour Hubble Space Telescope (HST) data. In particular we study how noise in the HST observations might impact such measurements and find this to be negligible. We determine the CG bias using HST observations in the F606W and F814W filters and observe a correlation with the colour, in line with expectations, whereas the dependence with redshift is weak. The biases for individual galaxies are generally well below 1%, which may be reduced further using morphological information from the Euclid data. Our results demonstrate that CG bias should not be ignored, but it is possible to determine its amplitude with sufficient precision, so that it will not significantly bias the weak lensing measurements using Euclid data.