Mapping Spatially Varying Additive Biases in Cosmic Shear Data
  In this paper we address the challenge of extracting maps of spatially
varying unknown additive biases from cosmic shear data. This is done by
exploiting the isotropy of the cosmic shear field, and the anisotropy of a
typical additive bias field, using an autocorrelation discrepancy map; which
identifies significant non-Gaussian components of the map. We test this
approach using simulations and find that the autocorrelation discrepancy map
produces spatially varying features that are indicative of the additive bias
field both in amplitude and spatial variation. We then apply this to the Dark
Energy Survey Year 1 data, and find evidence for spatially varying additive
biases of at most 0.002 on large-scales. The method can be used to empirically
inform modelling of the spatially varying additive bias field in any
cosmological parameter inference, and can act as a validation test for cosmic
shear surveys.
