Detecting the HI Power Spectrum in the Post-Reionization Universe with SKA-Low
Zhaoting Chen (1), Emma Chapman (2), Laura Wolz (1), Aishrila, Mazumder (1) ((1) Jodrell Bank Centre for Astrophysics, (2) University of, Nottingham)

TL;DR
This paper proposes a survey strategy using SKA-Low to detect the HI power spectrum at redshifts 5-6, emphasizing foreground removal techniques, integration times, and systematic effects to achieve reliable measurements.
Contribution
It demonstrates that Gaussian Process Regression outperforms PCA for foreground cleaning and establishes observational parameters for successful HI power spectrum detection with SKA-Low.
Findings
GPR outperforms PCA in foreground removal.
Integration time > 250 hours needed for accurate covariance modeling.
Detectable HI power spectrum at scales 0.02-0.3 Mpc^{-1} with SNR 2-5 in 600 hours.
Abstract
We present a survey strategy to detect the neutral hydrogen (HI) power spectrum at using the SKA-Low radio telescope in presence of foregrounds and instrumental effects. We simulate observations of the inherently weak HI signal post-reionization with varying levels of noise and contamination with foreground amplitudes equivalent to residuals after sky model subtraction. We find that blind signal separation methods on imaged data are required in order to recover the HI signal at large cosmological scales. Comparing different methods of foreground cleaning, we find that Gaussian Process Regression (GPR) performs better than Principle Component Analysis (PCA), with the key difference being that GPR uses smooth kernels for the total data covariance. The integration time of one field needs to be larger than h to provide large enough signal-to-noise ratio (SNR) to…
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Taxonomy
TopicsRadio Astronomy Observations and Technology · Astrophysics and Cosmic Phenomena · Cosmology and Gravitation Theories
