Day 1645

Thursday.  Friday.

1911.02195
FPFS Shear estimator: systematic tests on the Hyper Suprime-Cam Survey First Year Data
Li, Oguri, et al

We apply the Fourier Power Function Shapelets (FPFS) shear estimator to the first year data of the Hyper Suprime-Cam survey to construct a shape catalog. The FPFS shear estimator has been demonstrated to have multiplicative bias less than one percent in the absence of blending, regardless of complexities of galaxy shapes, smears of point spread functions (PSFs) and contamination from noise. The blending bias is calibrated with realistic image simulations, which include the impact of neighboring objects, using the COSMOS Hubble Space Telescope images. Here we carefully test the influence of PSF model residual on the FPFS shear estimation and the uncertainties in the shear calibration. Internal null tests are conducted to characterize potential systematics in the FPFS shape catalog and the results are compared with those measured using a catalog where the shapes were estimated using the re-Gaussianization algorithms. Furthermore, we compare various weak lensing measurements between the FPFS shape catalog and the re-Gaussianization shape catalog and conclude that the weak lensing measurements between these two shape catalogs are consistent with each other within the statistical uncertainty.

1911.02505

Metadetection: mitigating shear-dependent object detection biases with Metacalibration

Sheldon, et al

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 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 and measurement on the sheared images in order to calculate a shear response. We show that the method works up to second-order shear effects even in highly blended scenes. However, because the space between objects is sheared coherently in metadetection, the accuracy is ultimately limited by how closely this process matches real data, in which some, but not all, galaxies images are sheared coherently. We find that even for the worst case scenario, in which the space between objects is completely unsheared, the bias is at most a few tenths of a percent for future surveys. We show that the primary technical challenge for metadetection, deconvolution using a spatially varying point-spread-function, does not result in a significant bias for typical imaging surveys. Finally, we discuss additional technical challenges that must be met in order to implement metadetection for real surveys.