Large-scale power loss in ground-based CMB mapmaking

TL;DR

This paper reveals that ground-based CMB mapmaking can suffer from large-scale power loss due to model biases like sub-pixel errors and calibration mismatches, which are often missed by standard simulations.

Contribution

It identifies mechanisms causing large-scale bias in CMB mapmaking and proposes methods to detect and mitigate these biases.

Findings

Large-scale power loss can occur due to model biases in ground-based CMB mapmaking.

Sub-pixel errors and calibration mismatches are key mechanisms causing this bias.

Simple testing methods can detect the presence of large-scale model error bias.

Abstract

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. We show that the specific case of subpixel bias can be eliminated using bilinear pointing matrices, but also provide simple methods for testing for the presence of large-scale model error bias in general.