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The Atacama Cosmology Telescope: Component-separated maps of CMB temperature and the thermal Sunyaev-Zel'dovich effect
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Optimal analyses of many signals in the cosmic microwave background (CMB) require map-level extraction of individual components in the microwave sky, rather than measurements at the power spectrum level alone. To date, nearly all map-level component separation in CMB analyses has been performed exclusively using satellite data. In this paper, we implement a component separation method based on the internal linear combination (ILC) approach which we have designed to optimally account for the anisotropic noise (in the 2D Fourier domain) often found in ground-based CMB experiments. Using this method, we combine multi-frequency data from the Planck satellite and the Atacama Cosmology Telescope Polarimeter (ACTPol) to construct the first wide-area, arcminute-resolution component-separated maps (covering approximately 2100 sq. deg.) of the CMB temperature anisotropy and the thermal Sunyaev-Zel'dovich (tSZ) effect sourced by the inverse-Compton scattering of CMB photons off hot, ionized gas. Our ILC pipeline allows for explicit deprojection of various contaminating signals, including a modified blackbody approximation of the cosmic infrared background (CIB) spectral energy distribution. The cleaned CMB maps will be a useful resource for CMB lensing reconstruction, kinematic SZ cross-correlations, and primordial non-Gaussianity studies. The tSZ maps will be used to study the pressure profiles of galaxies, groups, and clusters through cross-correlations with halo catalogs, with dust contamination controlled via CIB deprojection. The data products described in this paper are available on LAMBDA.
Forward citations
Cited by 3 Pith papers
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Minimizing Contaminant Leakage in Internal Linear Combination Maps Using a Data-Driven Approach
Data-driven selection of the effective CIB spectral index per multipole bin enables unbiased, higher signal-to-noise tSZ-halo cross-correlations without moment deprojection.
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Single Frequency CMB Foreground Removal with Inter-scale Machine Learning
A hybrid CNN using both inter-scale and multi-frequency dust correlations achieves residual B-mode foreground power 3.62e-4 in DustFilaments simulations, about 7x lower than spatial ILC.
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Enhanced foreground mitigation in thermal SZ Compton-$y$ maps via polarization and deprojection
Combining temperature and polarization in a needlet ILC cuts residual Galactic contamination in the Planck thermal SZ y-map by about 40% (IRAS cross-correlation), with larger projected gains for future low-noise surveys.
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