Calibrated Ultra Fast Image Simulations for the Dark Energy Survey
  Weak lensing by large-scale structure is a powerful technique to probe the
dark components of the universe. To understand the measurement process of weak
lensing and the associated systematic effects, image simulations are becoming
increasingly important. For this purpose we present a first implementation of
the $\textit{Monte Carlo Control Loops}$ ($\textit{MCCL}$; Refregier & Amara
2014), a coherent framework for studying systematic effects in weak lensing. It
allows us to model and calibrate the shear measurement process using image
simulations from the Ultra Fast Image Generator (UFig; Berge et al. 2013). We
apply this framework to a subset of the data taken during the Science
Verification period (SV) of the Dark Energy Survey (DES). We calibrate the UFig
simulations to be statistically consistent with DES images. We then perform
tolerance analyses by perturbing the simulation parameters and study their
impact on the shear measurement at the one-point level. This allows us to
determine the relative importance of different input parameters to the
simulations. For spatially constant systematic errors and six simulation
parameters, the calibration of the simulation reaches the weak lensing
precision needed for the DES SV survey area. Furthermore, we find a sensitivity
of the shear measurement to the intrinsic ellipticity distribution, and an
interplay between the magnitude-size and the pixel value diagnostics in
constraining the noise model. This work is the first application of the
$\textit{MCCL}$ framework to data and shows how it can be used to methodically
study the impact of systematics on the cosmic shear measurement.
