Shear measurement bias I: dependencies on methods, simulation parameters
  and measured parameters
  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 Sersic index n as well as a different
shear bias between galaxies consisting of a single Sersic 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.
