Shape measurement biases from underfitting and ellipticity gradients

TL;DR

This paper identifies and addresses key biases in galaxy shape measurement for weak lensing, introducing a new method that confines analysis to observable k-space regions, significantly reducing biases and improving accuracy.

Contribution

The paper introduces a novel shape-measurement technique that minimizes underfitting and ellipticity gradient biases by focusing on observable k-space regions, achieving high accuracy without recalibration.

Findings

Biases are caused by high-frequency information loss and ellipticity gradients.

The new method reduces biases by factors of 20-100.

Achieves shear estimation errors below 1 part in 1000, outperforming previous methods.

Abstract

Precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in 1000. We investigate measurement biases, noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to shape measurement methodologies that rely upon fitting elliptical-isophote galaxy models to observed data. The first bias arises when the true galaxy shapes do not match the models being fit. We show that this "underfitting bias" is due, at root, to these methods' attempts to use information at high spatial frequencies that has been destroyed by the convolution with the point-spread function (PSF) and/or by sampling. We propose a new shape-measurement technique that is explicitly confined to observable regions of k-space. A second bias arises for galaxies whose ellipticity varies with radius. For most shape-measurement methods, such galaxies are subject to "ellipticity gradient bias". We show how to reduce such biases by factors of 20--100 within the new shape-measurement method. The resulting shear estimator has multiplicative errors <1 part in 1000 for high-S/N images, even for highly asymmetric galaxies. Without any training or recalibration, the new method obtains Q=3000 in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies, several times better than any previous method.