http://arxiv.org/abs/1702.02601
Metacalibration is a recently introduced method to accurately measure weak gravitational lensing shear using only the available imaging data, without need for prior information about galaxy properties or calibration from simulations. The method involves distorting the image with a small known shear, and calculating the response of a shear estimator to that applied shear. The method was shown to be accurate in moderate sized simulations with relatively high signal-to-noise galaxy images, and without significant selection effects. In this work we introduce a formalism to correct for both shear response and selection biases. We also observe that, for relatively low signal-to-noise images, the correlated noise that arises during the metacalibration process results in significant bias, for which we develop a simple empirical correction. To test this formalism, we use large simulations based on both parametric models and real galaxy images, including tests with realistic point-spread-functions. We introduce additional challenges that arise in real data, such as detection thresholds, stellar contamination, and missing data. We apply cuts on the galaxy properties to induce significant selection effects. Using our formalism, we recover the input shear with an accuracy better than a part in a thousand in all cases.
E. Sheldon and E. Huff
Fri, 10 Feb 17
8/46
Comments: 16 pages, 9 figures
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