The Atacama Cosmology Telescope: map-based noise simulations for DR6

Journal article

The increasing statistical power of cosmic microwave background (CMB) datasets requires a commensurate effort in understanding their noise properties. The noise in maps from ground-based instruments is dominated by large-scale correlations, which poses a modeling challenge. This paper develops novel models of the complex noise covariance structure in the Atacama Cosmology Telescope Data Release 6 (ACT DR6) maps. We first enumerate the noise properties that arise from the combination of the atmosphere and the ACT scan strategy. We then prescribe a class of Gaussian, map-based noise models, including a new wavelet-based approach that uses directional wavelet kernels for modeling correlated instrumental noise. The models are empirical, whose only inputs are a small number of independent realizations of the same region of sky. We evaluate the performance of these models against the ACT DR6 data by drawing ensembles of noise realizations. Applying these simulations to the ACT DR6 power spectrum pipeline reveals a $\sim 20\%$ excess in the covariance matrix diagonal when compared to an analytic expression that assumes noise properties are uniquely described by their power spectrum. Along with our public code, $\mathtt{mnms}$, this work establishes a necessary element in the science pipelines of both ACT DR6 and future ground-based CMB experiments such as the Simons Observatory (SO).41 pages (+10 appendix), 22 figures (+5 appendix), submitted to JCA

Authors: Atkins, Z; Duivenvoorden, AJ; Coulton, WR; Qu, FJ; Aiola, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOP Publishing
• Journal: Journal of Cosmology and Astroparticle Physics
• Volume: 2023
• Issue: 11
• Pages: 073-073
• Publication date: 2023-11-15
• DOI: 10.1088/1475-7516/2023/11/073
• ISSN: 1475-7508
• Language: English
• Keywords: Artificial intelligence; Optics; Telecommunications; Astrophysics; Statistical physics; Cosmology; Spectral density; Computer science; Cosmic microwave background; Noise (video); Algorithm; Covariance matrix; Covariance; Statistics; Gaussian noise; Physics