Mapping and simulating systematics due to spatially-varying observing
  conditions in DES Science Verification data
  Spatially-varying depth and characteristics of observing conditions, such as
seeing, airmass, or sky background, are major sources of systematic
uncertainties in modern galaxy survey analyses, in particular in deep
multi-epoch surveys. We present a framework to extract and project these
sources of systematics onto the sky, and apply it to the Dark Energy Survey
(DES) to map the observing conditions of the Science Verification (SV) data.
The resulting distributions and maps of sources of systematics are used in
several analyses of DES SV to perform detailed null tests with the data, and
also to incorporate systematics in survey simulations. We illustrate the
complementarity of these two approaches by comparing the SV data with the
BCC-UFig, a synthetic sky catalogue generated by forward-modelling of the DES
SV images. We analyse the BCC-UFig simulation to construct galaxy samples
mimicking those used in SV galaxy clustering studies. We show that the
spatially-varying survey depth imprinted in the observed galaxy densities and
the redshift distributions of the SV data are successfully reproduced by the
simulation and well-captured by the maps of observing conditions. The combined
use of the maps, the SV data and the BCC-UFig simulation allows us to quantify
the impact of spatial systematics on $N(z)$, the redshift distributions
inferred using photometric redshifts. We conclude that spatial systematics in
the SV data are mainly due to seeing fluctuations and are under control in
current clustering and weak lensing analyses. The framework presented here is
relevant to all multi-epoch surveys, and will be essential for exploiting
future surveys such as the Large Synoptic Survey Telescope (LSST), which will
require detailed null-tests and realistic end-to-end image simulations to
correctly interpret the deep, high-cadence observations of the sky.
