Mitigating Shear-dependent Object Detection Biases with Metacalibration

TL;DR

This paper introduces metadetection, an extension of metacalibration that accounts for shear-dependent object detection biases, enabling more accurate weak lensing shear measurements in crowded galaxy fields.

Contribution

The paper develops and tests metadetection, a novel method incorporating object detection into metacalibration to eliminate shear-dependent detection biases in galaxy images.

Findings

Metadetection accurately recovers shear signals in blended galaxy scenes.

Shear bias is reduced to less than a few tenths of a percent with the new method.

Detection biases are the main source of shear measurement bias in crowded fields.

Abstract

Metacalibration is a new technique for measuring weak gravitational lensing shear that is unbiased for isolated galaxy images. In this work we test metacalibration with overlapping, or ``blended'' galaxy images. Using standard metacalibration, we find a few percent shear measurement bias for galaxy densities relevant for current surveys, and that this bias increases with increasing galaxy number density. We show that this bias is not due to blending itself, but rather to shear-dependent object detection. If object detection is shear independent, no deblending of images is needed, in principle. We demonstrate that detection biases are accurately removed when including object detection in the metacalibration process, a technique we call metadetection. This process involves applying an artificial shear to images of small regions of sky and performing detection on the sheared images, as well as measurements that are used to calculate a shear response. We demonstrate that the method can accurately recover weak shear signals even in highly blended scenes. In the metacalibration process, the space between objects is sheared coherently, which does not perfectly match the real universe in which some, but not all, galaxy images are sheared coherently. We find that even for the worst case scenario, in which the space between objects is completely unsheared, the resulting shear bias is at most a few tenths of a percent for future surveys. We discuss additional technical challenges that must be met in order to implement metadetection for real surveys.