Mitigating Shear-dependent Object Detection Biases with Metacalibration

Metacalibration is a new technique for measuring weak gravitational lensingshear that is unbiased for isolated galaxy images. In this work we testmetacalibration with overlapping, or “blended” galaxy images. Using standardmetacalibration, we find a few percent shear measurement bias for galaxydensities relevant for current surveys, and that this bias increases withincreasing galaxy number density. We show that this bias is not due to blendingitself, but rather to shear-dependent object detection. If object detection isshear independent, no deblending of images is needed, in principle. Wedemonstrate that detection biases are accurately removed when including objectdetection in the metacalibration process, a technique we call metadetection.This process involves applying an artificial shear to images of small regionsof sky and performing detection on the sheared images, as well as measurementsthat are used to calculate a shear response. We demonstrate that the method canaccurately recover weak shear signals even in highly blended scenes. In themetacalibration process, the space between objects is sheared coherently, whichdoes not perfectly match the real universe in which some, but not all, galaxyimages are sheared coherently. We find that even for the worst case scenario,in which the space between objects is completely unsheared, the resulting shearbias is at most a few tenths of a percent for future surveys. We discussadditional technical challenges that must be met in order to implementmetadetection for real surveys.