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4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

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years

2026 2 2025 2

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UNVERDICTED 4

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representative citing papers

Non-Gaussianity in SMICA

astro-ph.CO · 2025-11-27 · unverdicted · novelty 7.0

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.

Forecasts of CMB $E$-mode anomalies for AliCPT-1

astro-ph.CO · 2026-04-22 · unverdicted · novelty 4.0

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.

citing papers explorer

Showing 4 of 4 citing papers.

  • Non-Gaussianity in SMICA astro-ph.CO · 2025-11-27 · unverdicted · none · ref 38

    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 astro-ph.IM · 2025-08-29 · unverdicted · none · ref 20

    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 astro-ph.CO · 2026-04-22 · unverdicted · none · ref 46

    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 astro-ph.CO · 2026-04-15 · unverdicted · none · ref 51

    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.