Tianlai 21 cm data is cleaned via multi-scale PCA-wavelet foreground subtraction and analyzed with spherical Fourier-Bessel decomposition to recover clustering signals without flat-sky biases.
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Simulations show hybrid foreground mitigation (GPR + PCA combined with avoidance) recovers the HI 21cm signal within 2σ for gain calibration errors ≤1% in SKA1-Low AA* observations over 0.05-0.5 Mpc^{-1} scales.
Review chapter organizing machine learning methods for 21 cm cosmology into observation, theory, and inference domains.
citing papers explorer
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Foreground Mitigation and Power Spectrum Analysis for Tianlai Full-Sky 21 cm Survey Observation
Tianlai 21 cm data is cleaned via multi-scale PCA-wavelet foreground subtraction and analyzed with spherical Fourier-Bessel decomposition to recover clustering signals without flat-sky biases.
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Mitigating gain calibration errors from EoR observations with SKA1-Low AA*
Simulations show hybrid foreground mitigation (GPR + PCA combined with avoidance) recovers the HI 21cm signal within 2σ for gain calibration errors ≤1% in SKA1-Low AA* observations over 0.05-0.5 Mpc^{-1} scales.
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Application of Machine Learning to 21 cm Cosmology
Review chapter organizing machine learning methods for 21 cm cosmology into observation, theory, and inference domains.