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...

Celý popis

Podrobná bibliografia

Vydané v:	Monthly notices of the Royal Astronomical Society Ročník 510; číslo 4; s. 5872 - 5890
Hlavní autori:	Soares, Paula S, Watkinson, Catherine A, Cunnington, Steven, Pourtsidou, Alkistis
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Oxford University Press 01.03.2022
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!