Modelling the Transfer Function for the Dark Energy Survey
  We present a forward-modelling simulation framework designed to model the
data products from the Dark Energy Survey (DES). This forward-model process can
be thought of as a transfer function -- a mapping from cosmological and
astronomical signals to the final data products used by the scientists. Using
output from the cosmological simulations (the Blind Cosmology Challenge), we
generate simulated images (the Ultra Fast Image Simulator, Berge et al. 2013)
and catalogs representative of the DES data. In this work we simulate the 244
sq. deg coadd images and catalogs in 5 bands for the DES Science Verification
(SV) data. The simulation output is compared with the corresponding data to
show that major characteristics of the images and catalogs can be captured. We
also point out several directions of future improvements. Two practical
examples, star/galaxy classification and proximity effects on object detection,
are then used to demonstrate how one can use the simulations to address
systematics issues in data analysis. With clear understanding of the
simplifications in our model, we show that one can use the simulations
side-by-side with data products to interpret the measurements. This forward
modelling approach is generally applicable for other upcoming and future
surveys. It provides a powerful tool for systematics studies which is
sufficiently realistic and highly controllable.
