Gaussian Process Regression for foreground removal in H i Intensity Mapping experiments

We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift H i intensity mapping, and present an open-source python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21 cm foregrounds (including polarization leakage), H i 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 H i 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 H i intensity mapping in the absence of missing frequency channels. Our python toolkit gpr4im and the data used in this analysis are publicly available on GitHub.

Automated summary

This study applies Gaussian Process Regression (GPR) for foreground removal in single-dish, low redshift H i intensity mapping for the first time, and provides an open-source Python toolkit for this purpose. The results show that GPR can be used effectively as a foreground removal technique, outperforming PCA in recovering the H i power spectrum, particularly on small scales. GPR performs better in recovering the radial power spectrum and is an excellent option for foreground removal in single-dish, low-redshift H i intensity mapping.