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.
A full sky, low foreground, high resolution CMB map from WMAP
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The WMAP satellite has made available high quality maps of the sky in five frequency bands ranging from 22 to 94 GHz, with the main scientific objective of studying the anisotropies of the Cosmic Microwave Background (CMB). These maps, however, contain a mixture of emissions from various astrophysical origins, superimposed on CMB emission. The objective of the present work is to make a high resolution CMB map in which contamination by such galactic and extra-galactic foregrounds, as well as by instrumental noise, is as low as possible. The method used is an implementation of a constrained linear combination of the channels with minimum error variance, and of Wiener filtering, on a frame of spherical wavelets called needlets, allowing localised filtering in both pixel space and harmonic space. We obtain a low contamination low noise CMB map at the resolution of the WMAP W channel, which can be used for a range of scientific studies. We obtain also a Wiener-filtered version with minimal integrated error. The resulting CMB maps offer significantly better rejection of galactic foregrounds than previous CMB maps from WMAP data. They can be considered as the most precise full-sky CMB temperature maps to-date.
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astro-ph.CO 4years
2026 4representative 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.
- Forecasts of CMB $E$-mode anomalies for AliCPT-1