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Publication

Mitigating Shear-dependent Object Detection Biases with Meta

Authors

Sheldon, Erin S. ; Becker, Matthew ; MacCrann, Niall; Jarvis, Michael

Abstract

Metacalibrationis a new technique for measuring weak gravitational lensing shear that is unbiased for isolated galaxy images. In this work we testmetacalibrationwith overlapping, or blended” galaxy images. Using standardmetacalibration, 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 themetacalibration process, a technique we callmetadetection. 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 themetacalibrationprocess, 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 implementmetadetectionfor real surveys.

Division

HEP

Publication Year

2020

Publication Type

Article

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