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.
preprint (arXiv:2108.01444) , year=
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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.
A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.
Matched-complexity foreground models recover the 21cm signal from simulated REACH skies, but parameter degeneracies limit free-free maps while synchrotron maps remain usable.
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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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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Forecasts of CMB $E$-mode anomalies for AliCPT-1
A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.
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Synchrotron and free-free mapping with simulated REACH observations between 50-170 MHz
Matched-complexity foreground models recover the 21cm signal from simulated REACH skies, but parameter degeneracies limit free-free maps while synchrotron maps remain usable.
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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.