English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

One of the primary challenges in enabling the scientific potential of 21 cmintensity mapping at the Epoch of Reionization (EoR) is the separation ofastrophysical foreground contamination. Recent works have claimed that Gaussianprocess regression (GPR) can robustly perform this separation, particularly atlow Fourier $k$ wavenumbers where the signal reaches its peak signal-to-noiseratio. We revisit this topic by casting GPR foreground subtraction (GPR-FS)into the quadratic estimator formalism, thereby putting its statisticalproperties on stronger theoretical footing. We find that GPR-FS can distort thewindow functions at these low k modes, which, without proper decorrelation,make it difficult to probe the EoR power spectrum. Incidentally, we also showthat GPR-FS is in fact closely related to the widely studied optimal quadraticestimator. As a case study, we look at recent power spectrum upper limits fromthe Low Frequency Array (LOFAR) that utilized GPR-FS. We pay close attention totheir normalization scheme, showing that it is particularly sensitive to signalloss when the EoR covariance is misestimated. This implies possibleramifications for recent astrophysical interpretations of the LOFAR limits,because many of the EoR models ruled out do not fall within the bounds of thecovariance models explored by LOFAR. Being more robust to this bias (althoughnot entirely free of it), we conclude that the quadratic estimator is a morenatural framework for implementing GPR-FS and computing the 21 cm powerspectrum.

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