Statistical 21-cm Signal Separation via Gaussian Process Regression Analysis

Abstract

Detecting and characterizing the Epoch of Reionization and Cosmic Dawn via the redshifted 21-cm hyperfine line of neutral hydrogen will revolutionize the study of the formation of the first stars, galaxies, black holes and intergalactic gas in the infant Universe. The wealth of information encoded in this signal is, however, buried under foregrounds that are many orders of magnitude brighter. These must be removed accurately and precisely in order to reveal the feeble 21-cm signal. This requires not only the modeling of the Galactic and extra-galactic emission, but also of the often stochastic residuals due to imperfect calibration of the data caused by ionospheric and instrumental distortions. To stochastically model these effects, we introduce a new method based on `Gaussian Process Regression' (GPR) which is able to statistically separate the 21-cm signal from most of the foregrounds and other contaminants. Using simulated LOFAR-EoR data that include strong instrumental mode-mixing, we show that this method is capable of recovering the 21-cm signal power spectrum across the entire range $k = 0.07 - 0.3 \ \rm{h\, cMpc^{-1}}$. The GPR method is most optimal, having minimal and controllable impact on the 21-cm signal, when the foregrounds are correlated on frequency scales $\gtrsim 3$\,MHz and the rms of the signal has $σ_{\mathrm{21cm}} \gtrsim 0.1\,σ_{\mathrm{noise}}$. This signal separation improves the 21-cm power-spectrum sensitivity by a factor $\gtrsim 3$ compared to foreground avoidance strategies and enables the sensitivity of current and future 21-cm instruments such as the {\sl Square Kilometre Array} to be fully exploited.