Gaussian Process Foreground Subtraction and Power Spectrum Estimation for 21 cm Cosmology

Understand

One of the primary challenges in enabling the scientific potential of 21 cm intensity mapping at the Epoch of Reionization (EoR) is the separation of astrophysical foreground contamination.

• Recent works have claimed that Gaussian process regression (GPR) can robustly perform this separation, particularly at low Fourier $k$ wavenumbers where the signal reaches its peak signal-to-noise ratio.
• We revisit this topic by casting GPR foreground subtraction (GPR-FS) into the quadratic estimator formalism, thereby putting its statistical properties on stronger theoretical footing.
• We find that GPR-FS can distort the window functions at these low k modes, which, without proper decorrelation, make it difficult to probe the EoR power spectrum.