Gaussian Process Regression for foreground removal in H i 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 (inclu...

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Bibliographic Details

Published in:	Monthly notices of the Royal Astronomical Society Vol. 510; no. 4; pp. 5872 - 5890
Main Authors:	Soares, Paula S, Watkinson, Catherine A, Cunnington, Steven, Pourtsidou, Alkistis
Format:	Journal Article
Language:	English
Published:	London Oxford University Press 01.03.2022
Subjects:	Channels Context Frequency ranges Gaussian process Mapping Principal components analysis Red shift Toolkits radio lines: general cosmology: observations methods: data analysis large-scale structure of Universe
ISSN:	0035-8711, 1365-2966
Online Access:	Get full text
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