Improving Weak Lensing Mass Map Reconstructions using Gaussian and
  Sparsity Priors: Application to DES SV
  Mapping the underlying density field, including non-visible dark matter,
using weak gravitational lensing measurements is now a standard tool in
cosmology. Due to its importance to the science results of current and upcoming
surveys, the quality of the convergence reconstruction methods should be well
understood. We compare three methods: Kaiser-Squires (KS), Wiener filter, and
GLIMPSE. KS is a direct inversion, not accounting for survey masks or noise.
The Wiener filter is well-motivated for Gaussian density fields in a Bayesian
framework. GLIMPSE uses sparsity, aiming to reconstruct non-linearities in the
density field. We compare these methods with several tests using public Dark
Energy Survey (DES) Science Verification (SV) data and realistic DES
simulations. The Wiener filter and GLIMPSE offer substantial improvements over
smoothed KS with a range of metrics. Both the Wiener filter and GLIMPSE
convergence reconstructions show a 12 per cent improvement in Pearson
correlation with the underlying truth from simulations. To compare the mapping
methods' abilities to find mass peaks, we measure the difference between peak
counts from simulated {\Lambda}CDM shear catalogues and catalogues with no mass
fluctuations (a standard data vector when inferring cosmology from peak
statistics); the maximum signal-to-noise of these peak statistics is increased
by a factor of 3.5 for the Wiener filter and 9 for GLIMPSE. With simulations we
measure the reconstruction of the harmonic phases; the phase residuals'
concentration is improved 17 per cent by GLIMPSE and 18 per cent by the Wiener
filter. The correlation between reconstructions from data and foreground
redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per
cent by GLIMPSE.
