| dc.contributor.author | Soares, Paula S. | |
| dc.contributor.author | Watkinson, Catherine A. | |
| dc.contributor.author | Pourtsidou, Alkistis | |
| dc.date.accessioned | 2022-10-06T09:56:17Z | |
| dc.date.available | 2022-10-06T09:56:17Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Soares, 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/stab2594 | en_US |
| dc.identifier.issn | 1365-2966 | |
| dc.identifier.uri | https://doi.org/10.1093/mnras/stab2594 | |
| dc.identifier.uri | http://hdl.handle.net/10566/8026 | |
| dc.description.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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Oxford University Press | en_US |
| dc.subject | Astronomy | en_US |
| dc.subject | Astrophysics | en_US |
| dc.subject | Cosmology | en_US |
| dc.subject | Radio lines | en_US |
| dc.subject | MeerKAT | en_US |
| dc.title | Gaussian process regression for foreground removal in hi intensity mapping experiments | en_US |
| dc.type | Article | en_US |
