Soares, Paula S.Watkinson, Catherine A.Pourtsidou, Alkistis2022-10-062022-10-062022Soares, P. S. et al. (2022). Gaussian process regression for foreground removal in hi intensity mapping experiments. Monthly Notices of the Royal Astronomical Society, 510,(4), 5872–5890. https://doi.org/10.1093/mnras/stab25941365-2966https://doi.org/10.1093/mnras/stab2594http://hdl.handle.net/10566/8026We 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.enAstronomyAstrophysicsCosmologyRadio linesMeerKATArticle