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2022

Large-scale power loss in ground-based CMB mapmaking

Naess, Sigurd, Louis, Thibaut

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CMB mapmaking relies on a data model to solve for the sky map, and this process is vulnerable to bias if the data model cannot capture the full behavior of the signal.

We demonstrate that this bias is not just limited to small-scale effects in high-contrast regions of the sky, but can manifest as $\mathcal{O}(1)$ power loss on large scales in the map under conditions and assumptions realistic for ground-based CMB telescopes.
This bias is invisible to simulation-based tests that do not explicitly model them, making it easy to miss.
We identify two different mechanisms that both cause suppression of long-wavelength modes: sub-pixel errors and detector gain calibration mismatch.

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