Work

Large-scale power loss in ground-based CMB mapmaking

Year: 2023

Type: article

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 investigate the common cases of sub-pixel errors and gain errors in more detail and demonstrate that subpixel bias can be eliminated using bilinear pointing matrices. Finally, we provide simple methods for testing for the presence of large-scale model error bias in the general case.

Source: The Open Journal of Astrophysics

DOI: 10.21105/astro.2210.02243

Authors: Sigurd Naess, Thibaut Louis

Institutions: University of Oslo, Centre National de la Recherche Scientifique, Université Paris-Saclay, Institut National de Physique Nucléaire et de Physique des Particules, Laboratoire de Physique des 2 Infinis Irène Joliot-Curie

Language: English

Topic: Cosmology and Gravitation Theories

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

Funder: Simons Foundation

Award: G1271232467