Abstract
We present a study of the dependencies of shear bias on simulation (input) and measured (output) parameters, noise, point-spread function anisotropy, pixel size, and the model bias coming from two different and independent galaxy shape estimators. We used simulated images from GALSIM based on the GREAT3 control-space-constant branch, and we measured shear bias from a model-fitting method (GFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We show the bias dependencies found on input and output parameters for both methods, and we identify the main dependencies and causes. Most of the results are consistent between the two estimators, an interesting result given the differences of the methods. We also find important dependences on orientation and morphology properties such as flux, size, and ellipticity. We show that noise and pixelization play an important role in the bias dependencies on the output properties and galaxy orientation. We show some examples of model bias that produce a bias dependence on the Sérsic index n as well as a different shear bias between galaxies consisting of a single Sérsic profile and galaxies with a disc and a bulge. We also see an important coupling between several properties on the bias dependences. Because of this, we need to study several measured properties simultaneously in order to properly understand the nature of shear bias. This paper serves as a first step towards a companion paper that describes a machine learning approach to modelling shear bias as a complex function of many observed properties.
Highlights
Weak gravitational lensing is a powerful and promising probe of cosmology for current and upcoming galaxy surveys, such as the Hyper Suprime-Cam (HSC; Miyazaki et al 2006), the Dark Energy Survey (DES; The Dark Energy Survey Collaboration 2005; Flaugher 2005), the Kilo Degree Survey (KIDS; de Jong et al 2013), the Large Synoptic Survey Telescope (LSST; LSST Science Collaboration 2009), Euclid (Laureijs et al 2011), and the Wide-Field Infrared Survey Telescope (WFIRST; Green et al 2012)
Our goal is to identify the main dependencies of bias found as a function of all simulation and measured parameters, PSF anisotropy, noise, pixelization, and model bias coming from the use of different shear estimators
We show some examples of the differences between ellipticity bias, which describes the errors on the estimation of the shape of the images, and shear bias, which defines the errors obtained in the estimation of the shear of a given sample of galaxies
Summary
Weak gravitational lensing is a powerful and promising probe of cosmology for current and upcoming galaxy surveys, such as the Hyper Suprime-Cam (HSC; Miyazaki et al 2006), the Dark Energy Survey (DES; The Dark Energy Survey Collaboration 2005; Flaugher 2005), the Kilo Degree Survey (KIDS; de Jong et al 2013), the Large Synoptic Survey Telescope (LSST; LSST Science Collaboration 2009), Euclid (Laureijs et al 2011), and the Wide-Field Infrared Survey Telescope (WFIRST; Green et al 2012). In the GREAT3 Challenge (Mandelbaum et al 2015), different shape measurement methods were tested to infer weak lensing shear distortions from different simulated surveys (spaceand ground-based), shear variations (constant or cosmologically varying), and galaxy morphologies (realistic and parametric) They studied the bias dependencies on truncation due to finite postage stamps, the Sérsic index of the galaxy profiles, the PSF size, ellipticity and defocus, and the impact of the estimation and interpolation of the PSF. We studied the effect of isotropic and realistic PSFs, noise, and pixelization by repeating the measurements of the estimators on new realizations of the image simulations; here, we applied some variations on the noise variance and the use of either an isotropic Gaussian PSF or different realistic and anisotropic PSFs. We do not explore the dependencies of bias coming from implementation parameters, such as the minimization or initialization parameters or the choice of different galaxy models, in this paper.
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