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.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
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.
Forecasts indicate AliCPT combined with Simons Observatory can detect injected E-mode dipole modulation at 99% confidence, while AliCPT alone risks biases in alignment and parity tests due to limited sky coverage.
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
-
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.
-
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.
-
Forecasts of CMB $E$-mode anomalies for AliCPT-1
Forecasts indicate AliCPT combined with Simons Observatory can detect injected E-mode dipole modulation at 99% confidence, while AliCPT alone risks biases in alignment and parity tests due to limited sky coverage.
-
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.