A highly precise shear bias estimator independent of the measured shape noise
DEDIP/DAP, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur- Yvette, France
2 Université Paris Diderot, AIM, Sorbonne Paris Cité, CEA, CNRS, 91191 Gif-sur-Yvette, France
3 Institut d’Astrophysique de Paris, UMR7095 CNRS, Université Pierre & Marie Curie, 98 bis boulevard Arago, 75014 Paris, France
Accepted: 28 October 2018
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.
Key words: gravitational lensing: weak / methods: data analysis / methods: observational / methods: statistical / cosmology: observations / dark matter
© ESO 2018
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.