Abstract
We present a new method to estimate shear measurement bias in image simulations that significantly improves the precision with respect to current techniques. Our method is based on measuring the shear response for individual images. We generated sheared versions of the same image to measure how the galaxy shape changes with the small applied shear. This shear response is the multiplicative shear bias for each image. In addition, we also measured the individual additive bias. Using the same noise realizations for each sheared version allows us to compute the shear response at very high precision. The estimated shear bias of a sample of galaxies is then the average of the individual measurements. The precision of this method leads to an improvement with respect to previous methods concerned with the precision of estimates of multiplicative bias since our method is not affected by noise from shape measurements, which until now has been the dominant uncertainty. As a consequence, the method does not require shape-noise suppression for a precise estimation of shear multiplicative bias. Our method can be readily used for numerous applications such as shear measurement validation and calibration, reducing the number of necessary simulated images by a few orders of magnitude to achieve the same precision.
Highlights
Upcoming weak-lensing surveys have the goal of measuring cosmology with unprecedentedly high precision
In this paper we present a new method to estimate shear bias from image simulations
Our estimator of the multiplicative shear bias is not affected by shape noise and reduces the noise contribution from the measured shape, removing the dominant uncertainty in bias estimation
Summary
Upcoming weak-lensing surveys have the goal of measuring cosmology with unprecedentedly high precision. One of the main sources of systematic error for weak gravitational lensing is the bias in the measurement of the galaxy shear, which carries the cosmological information about the galaxy’s large-scale structure and its evolution. For upcoming experiments such as Euclid (Laureijs et al 2011), the Large Synoptic Survey Telescope (LSST, LSST Science Collaboration 2009), or the Wide Field Infrared Survey Telescope (WFIRST, Spergel et al 2013), we need to calibrate shear biases to sub-percent precision. This has a dramatic impact on the computation load of generating both the simulations and shape measurement methods, and on limiting the complexity, storage, and re-usability (e.g. Hoekstra et al 2017)
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