Two NILC extensions—one deprojecting foreground moments and one marginalizing residuals at the likelihood level—yield unbiased r estimates and consistent lensing B-mode reconstruction in SO-SAT-like simulations.
Foreground component separation with generalised ILC
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The 'Internal Linear Combination' (ILC) component separation method has been extensively used to extract a single component, the CMB, from the WMAP multifrequency data. We generalise the ILC approach for separating other millimetre astrophysical emissions. We construct in particular a multidimensional ILC filter, which can be used, for instance, to estimate the diffuse emission of a complex component originating from multiple correlated emissions, such as the total emission of the Galactic interstellar medium. The performance of such generalised ILC methods, implemented on a needlet frame, is tested on simulations of Planck mission observations, for which we successfully reconstruct a low noise estimate of emission from astrophysical foregrounds with vanishing CMB and SZ contamination.
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UNVERDICTED 3representative citing papers
Dust-cleaned CIB and CMB lensing cross-correlations yield f_NL^local = 43 ± 23, tightening constraints on local primordial non-Gaussianity.
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.
citing papers explorer
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Blind mitigation of foreground-induced biases on primordial $B$ modes for ground-based CMB experiments
Two NILC extensions—one deprojecting foreground moments and one marginalizing residuals at the likelihood level—yield unbiased r estimates and consistent lensing B-mode reconstruction in SO-SAT-like simulations.
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New constraints on primordial non-Gaussianity from large-scale cross-correlations of CMB lensing and the cosmic infrared background
Dust-cleaned CIB and CMB lensing cross-correlations yield f_NL^local = 43 ± 23, tightening constraints on local primordial non-Gaussianity.
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BROOM: a python package for model-independent analysis of microwave astronomical data
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.