Calibration of weak-lensing shear in the Kilo-Degree Survey
Ian Fenech Conti, Ricardo Herbonnet, Henk Hoekstra, Julian Merten,, Lance Miller, Massimo Viola

TL;DR
This paper presents a comprehensive calibration pipeline for weak lensing shear measurements in the KiDS survey, including novel self-calibration and bias mitigation techniques, validated through simulations to achieve less than 1% bias accuracy.
Contribution
It introduces a new self-calibration method for noise bias correction and a scheme to mitigate weight bias, improving shear measurement accuracy in weak lensing surveys.
Findings
Self-calibration significantly reduces shear biases.
Calibration achieves less than 1% multiplicative bias.
Residual biases are well-controlled across tomographic bins.
Abstract
We describe and test the pipeline used to measure the weak lensing shear signal from the Kilo Degree Survey (KiDS). It includes a novel method of `self-calibration' that partially corrects for the effect of noise bias. We also discuss the `weight bias' that may arise in optimally-weighted measurements, and present a scheme to mitigate that bias. To study the residual biases arising from both galaxy selection and shear measurement, and to derive an empirical correction to reduce the shear biases to , we create a suite of simulated images whose properties are close to those of the KiDS survey observations. We find that the use of `self-calibration' reduces the additive and multiplicative shear biases significantly, although further correction via a calibration scheme is required, which also corrects for a dependence of the bias on galaxy properties. We find that the…
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