kNN CDF statistics detect 21cm-galaxy cross-correlations more effectively than two-point methods and distinguish reionization models at fixed ionized fraction even with noise and foregrounds.
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Bayesian GPR recovers the 21cm signal within 2σ credible intervals for most k-modes (0.06 to 1.0 h/Mpc) in SKA1-Low simulations that include realistic residual foregrounds and systematics.
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.
citing papers explorer
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Nearest Neighbour-Based Statistics for 21cm-Galaxy Cross-Correlations in the Epoch of Reionization
kNN CDF statistics detect 21cm-galaxy cross-correlations more effectively than two-point methods and distinguish reionization models at fixed ionized fraction even with noise and foregrounds.
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Mitigating residual foregrounds and systematic errors in SKA1-Low AA* EoR observations via Bayesian Gaussian Process Regression
Bayesian GPR recovers the 21cm signal within 2σ credible intervals for most k-modes (0.06 to 1.0 h/Mpc) in SKA1-Low simulations that include realistic residual foregrounds and systematics.
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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.