Gaussian Process Regression for foreground removal in HI intensity mapping experiments

TLDR: In this paper, Gaussian Process Regression (GPR) is applied for the first time as a foreground removal technique in the context of single-dish, low redshift HI intensity mapping, and presented an open-source python toolkit for doing so.

Abstract: We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift HI intensity mapping, and present an open-source python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21cm foregrounds (including polarisation leakage), HI cosmological signal and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the HI power spectrum than Principal Component Analysis (PCA), especially on small scales. GPR is especially good at recovering the radial power spectrum, outperforming PCA when considering the full bandwidth of our data. Both methods are worse at recovering the transverse power spectrum, since they rely on frequency-only covariance information. When halving our data along frequency, we find that GPR performs better in the low frequency range, where foregrounds are brighter. It performs worse than PCA when frequency channels are missing, to emulate RFI flagging. We conclude that GPR is an excellent foreground removal option for the case of single-dish, low redshift HI intensity mapping. Our python toolkit gpr4im and the data used in this analysis are publicly available on GitHub. The GitHub symbol in the caption of each figure links to a jupyter notebook showing how the figure was produced.