Browsing by Author "Soares, Paula S."

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    Gaussian process regression for foreground removal in hi intensity mapping experiments
    (Oxford University Press, 2022) Soares, Paula S.; Watkinson, Catherine A.; Pourtsidou, Alkistis
    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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    Multipole expansion for H I intensity mapping experiments: simulations and modelling
    (Oxford University Press, 2020) Pourtsidou, Alkistis; Cunnington, Steven; Soares, Paula S.
    We present a framework and an open-source PYTHON toolkit to analyse the two-point statistics of 3D fluctuations in the context of H I intensity maps using the multipole expansion formalism. We include simulations of the cosmological H I signal using N-body and lognormal methods, foregrounds and their removal, as well as instrumental effects. Using these simulations and analytical modelling, we investigate the impact of foreground cleaning and the instrumental beam on the power spectrum multipoles as well as on the Fourier space clustering wedges. We find that both the instrumental beam and the foreground removal can produce a quadrupole (and a hexadecapole) signal, and demonstrate the importance of controlling and accurately modelling these effects for precision radio cosmology.