Mapping Spatially Varying Additive Biases in Cosmic Shear Data

Understand

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.