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In this paper we address the challenge of extracting maps of spatiallyvarying unknown additive biases from cosmic shear data. This is done byexploiting the isotropy of the cosmic shear field, and the anisotropy of atypical additive bias field, using an autocorrelation discrepancy map; whichidentifies significant non-Gaussian components of the map. We test thisapproach using simulations and find that the autocorrelation discrepancy mapproduces spatially varying features that are indicative of the additive biasfield both in amplitude and spatial variation. We then apply this to the DarkEnergy Survey Year 1 data, and find evidence for spatially varying additivebiases of at most 0.002 on large-scales. The method can be used to empiricallyinform modelling of the spatially varying additive bias field in anycosmological parameter inference, and can act as a validation test for cosmicshear surveys.

Mapping Spatially Varying Additive Biases in Cosmic Shear Data