Gaussian process regression for foreground removal in hi intensity mapping experiments

Abstract

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.