Foreground modelling via Gaussian process regression: an application to HERA data
Date Issued
2020
Author(s)
Abhik Ghosh
•
Florent Mertens
•
•
Mário G. Santos
•
Nicholas S. Kern
•
Christopher L. Carilli
•
Trienko L. Grobler
•
Léon V. E. Koopmans
•
Daniel C. Jacobs
•
Adrian Liu
•
Aaron R. Parsons
•
Miguel F. Morales
•
James E. Aguirre
•
Joshua S. Dillon
•
Bryna J. Hazelton
•
Oleg M. Smirnov
•
Bharat K. Gehlot
•
Siyanda Matika
•
Paul Alexander
•
Zaki S. Ali
•
Adam P. Beardsley
•
Roshan K. Benefo
•
Tashalee S. Billings
•
Judd D. Bowman
•
Richard F. Bradley
•
Carina Cheng
•
Paul M. Chichura
•
David R. DeBoer
•
Eloy de Lera Acedo
•
Aaron Ewall-Wice
•
Gcobisa Fadana
•
Nicolas Fagnoni
•
Austin F. Fortino
•
Randall Fritz
•
Steve R. Furlanetto
•
Samavarti Gallardo
•
Brian Glendenning
•
Deepthi Gorthi
•
Bradley Greig
•
Jasper Grobbelaar
•
Jack Hickish
•
Alec Josaitis
•
Austin Julius
•
Amy S. Igarashi
•
MacCalvin Kariseb
•
Saul A. Kohn
•
Matthew Kolopanis
•
Telalo Lekalake
•
Anita Loots
•
David MacMahon
•
Lourence Malan
•
Cresshim Malgas
•
Matthys Maree
•
Zachary E. Martinot
•
Nathan Mathison
•
Eunice Matsetela
•
Andrei Mesinger
•
Abraham R. Neben
•
Bojan Nikolic
•
Chuneeta D. Nunhokee
•
Nipanjana Patra
•
Samantha Pieterse
•
Nima Razavi-Ghods
•
Jon Ringuette
•
James Robnett
•
Kathryn Rosie
•
Raddwine Sell
•
Craig Smith
•
Angelo Syce
•
Max Tegmark
•
Nithyanandan Thyagarajan
•
Peter K. G. Williams
•
Haoxuan Zheng
Abstract
The key challenge in the observation of the redshifted 21-cm signal from
cosmic reionization is its separation from the much brighter foreground
emission. Such separation relies on the different spectral properties of the
two components, although, in real life, the foreground intrinsic spectrum is
often corrupted by the instrumental response, inducing systematic effects that
can further jeopardize the measurement of the 21-cm signal. In this paper, we
use Gaussian Process Regression to model both foreground emission and
instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of
Reionization Array. We find that a simple co-variance model with three
components matches the data well, giving a residual power spectrum with white
noise properties. These consist of an "intrinsic" and instrumentally corrupted
component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating
the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h
cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$
MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should
be distinguishable from the 21-cm EoR signal whose typical coherence-scales is
$\sim 0.8$ MHz.
Volume
495
Issue
3
Start page
2813
Issn Identifier
0035-8711
Rights
open.access
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