New SMICA formalism and binned bispectrum estimator jointly recover power spectra, spectral parameters, foreground 3-point correlators, and primordial non-Gaussianity constraints from multi-frequency polarization maps tested on LiteBIRD simulations.
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A U-Net GAN reconstructs CMB T and E maps from Planck-like simulations with foregrounds and systematics, achieving under 1% error outside the Galactic region and demonstrating first-time correction for non-circular beams and asymmetric scans.
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.
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.
Galactic foreground residuals after component separation bias lensing reconstruction errors at cosmic-variance levels with Gaussian terms dominating, while non-Gaussian errors are three orders smaller; residuals become a leading error for high-efficiency delensing.
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Non-Gaussianity in SMICA
New SMICA formalism and binned bispectrum estimator jointly recover power spectra, spectral parameters, foreground 3-point correlators, and primordial non-Gaussianity constraints from multi-frequency polarization maps tested on LiteBIRD simulations.
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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
A U-Net GAN reconstructs CMB T and E maps from Planck-like simulations with foregrounds and systematics, achieving under 1% error outside the Galactic region and demonstrating first-time correction for non-circular beams and asymmetric scans.
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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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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.
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Galactic foreground residue biases in cosmic-microwave-background lensing-convergence reconstruction and delensing of B-mode maps
Galactic foreground residuals after component separation bias lensing reconstruction errors at cosmic-variance levels with Gaussian terms dominating, while non-Gaussian errors are three orders smaller; residuals become a leading error for high-efficiency delensing.