Gaussian process regression for foreground removal in hi intensity mapping experiments

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soares_gaussian process regression_2022.pdf (2.71 MB)

Date

2022

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Oxford University Press

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.

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Keywords

Astronomy, Astrophysics, Cosmology, Radio lines, MeerKAT

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

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https://doi.org/10.1093/mnras/stab2594
 http://hdl.handle.net/10566/8026

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Research Articles (Physics)