Mitigating residual foregrounds and systematic errors in SKA1-Low AA* EoR observations via Bayesian Gaussian Process Regression

TL;DR

This study evaluates Bayesian Gaussian Process Regression's effectiveness in mitigating residual foregrounds and systematic errors in SKA1-Low EoR observations using realistic simulations.

Contribution

It extends machine-learning-based GPR to synthetic SKA1-Low data, assessing its robustness against various instrumental and environmental systematic errors.

Findings

21 cm signal can be robustly recovered within 2σ credible interval for most k-modes.

GPR frameworks effectively suppress residual foreground contamination.

Analysis demonstrates reliable uncertainty estimates across a range of scales.

Abstract

The redshifted 21\,cm line is an emerging tool in observational cosmology that can serve as a direct probe of the intergalactic medium throughout the cosmic timeline. However, the observation of the cosmological 21\,cm signal from early epochs is extremely challenging in practice, regardless of the scale of interest and redshift. The presence of bright astrophysical foregrounds and residual systematic errors along the line of sight poses challenges for its detection. Machine-learning-based Gaussian process regression\,(ML-GPR) has proven to be the most effective strategy for signal separation in LOFAR and NenuFAR observations to measure the 21\,cm signal power spectrum from the Cosmic Dawn\,(CD) and Epoch of Reionization\,(EoR). In this work, we extend this framework to synthetic CD/EoR SKA1-Low observations to assess its robustness in mitigating residual foregrounds against instrumental and environmental systematic effects. We use our developed end-to-end realistic simulation pipeline (\textsc{21cmE2E}) for SKA-Low observations. Our 4-hour tracking simulation includes extragalactic point sources, the AA* telescope configuration, primary beam response, and error models. The modelled errors incorporate residual antenna-based gain calibration errors, residual ionospheric phase errors, partial de-mixing of the out-of-field sources, and instrumental noise for 1000\,hours of deep integration time. We compare different Bayesian GPR frameworks to assess their ability to suppress residual foreground contamination while minimizing signal loss and providing reliable uncertainty estimates. Our analysis demonstrates that the 21\,cm signal can robustly recover within the $2\sigma$ credible interval for almost all k-modes over the range of $0.06 \leq k \leq 1.0$~h Mpc$^{-1}$.