Bayes-SCF: A Bayesian filter to mitigate foreground leakage in the 21-cm power spectrum
Khandakar Md Asif Elahi

TL;DR
This paper introduces Bayes-SCF, a Bayesian Gaussian Process-based method that effectively removes non-ergodic foregrounds from 21-cm cosmology data, improving power spectrum estimation over traditional methods.
Contribution
The paper presents Bayes-SCF, a novel Bayesian filtering technique using Gaussian Processes to better model and remove spectrally complex foregrounds in 21-cm observations.
Findings
Bayes-SCF accurately recovers the input power spectrum in simulations.
It outperforms Hann-window based SCF in the presence of short correlation length foregrounds.
The method is effective in delay-spectrum analysis.
Abstract
Missing channels in radio-interferometric visibility data can introduce systematic artifacts into the estimated 21-cm power spectrum. A common workaround is to first estimate the two-frequency correlation and then Fourier-transform it to obtain the power spectrum . This procedure yields an unbiased estimate when the signal is statistically homogeneous (ergodic) along the line-of-sight, but it fails in the presence of non-ergodic foregrounds. Smooth Component Filtering (SCF) has recently been proposed as a solution to this problem, in which the dominant non-ergodic (spectrally smooth) component is removed prior to estimating . In existing implementations, the smooth component is estimated by convolving the visibilities with a Hann window along the frequency axis. We demonstrate that this Hann-based SCF performs adequately only when foregrounds…
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Taxonomy
TopicsRadio Astronomy Observations and Technology · Millimeter-Wave Propagation and Modeling · Indoor and Outdoor Localization Technologies
