Multi-CCD Point Spread Function Modelling
  Galaxy imaging surveys observe a vast number of objects that are affected by
the instrument's Point Spread Function (PSF). Weak lensing missions, in
particular, aim at measuring the shape of galaxies, and PSF effects represent
an important source of systematic errors which must be handled appropriately.
This demands a high accuracy in the modelling as well as the estimation of the
PSF at galaxy positions. Sometimes referred to as non-parametric PSF
estimation, the goal of this paper is to estimate a PSF at galaxy positions,
starting from a set of noisy star image observations distributed over the focal
plane. To accomplish this, we need our model to first of all, precisely capture
the PSF field variations over the Field of View (FoV), and then to recover the
PSF at the selected positions. This paper proposes a new method, coined MCCD
(Multi-CCD PSF modelling), that creates, simultaneously, a PSF field model over
all of the instrument's focal plane. This allows to capture global as well as
local PSF features through the use of two complementary models which enforce
different spatial constraints. Most existing non-parametric models build one
model per Charge Coupled Device (CCD), which can lead to difficulties in
capturing global ellipticity patterns. We first test our method on a realistic
simulated dataset comparing it with two state-of-the-art PSF modelling methods
(PSFEx and RCA). We outperform both of them with our proposed method. Then we
contrast our approach with PSFEx on real data from CFIS (Canada France Imaging
Survey) that uses the CFHT (Canada-France-Hawaii Telescope). We show that our
PSF model is less noisy and achieves a 22% gain on pixel Root Mean Squared
Error (RMSE) with respect to PSFEx. We present, and share the code of, a new
PSF modelling algorithm that models the PSF field on all the focal plane that
is mature enough to handle real data.
