Foreground modelling via Gaussian process regression: an application to HERA data
Creators: name: Ghosh, Abhik
name: Mertens, Florent
name: Bernardi, Gianni
name: Santos, Mário G.
name: Kern, Nicholas S.
name: Carilli, Christopher L.
name: Grobler, Trienko L.
name: Koopmans, Léon V. E.
name: Jacobs, Daniel C.
name: Liu, Adrian
name: Parsons, Aaron R.
name: Morales, Miguel F.
name: Aguirre, James E.
name: Dillon, Joshua S.
name: Hazelton, Bryna J.
name: Smirnov, Oleg M.
name: Gehlot, Bharat K.
name: Matika, Siyanda
name: Alexander, Paul
name: Ali, Zaki S.
name: Beardsley, Adam P.
name: Benefo, Roshan K.
name: Billings, Tashalee S.
name: Bowman, Judd D.
name: Bradley, Richard F.
name: Cheng, Carina
name: Chichura, Paul M.
name: DeBoer, David R.
name: Acedo, Eloy de Lera
name: Ewall-Wice, Aaron
name: Fadana, Gcobisa
name: Fagnoni, Nicolas
name: Fortino, Austin F.
name: Fritz, Randall
name: Furlanetto, Steve R.
name: Gallardo, Samavarti
name: Glendenning, Brian
name: Gorthi, Deepthi
name: Greig, Bradley
name: Grobbelaar, Jasper
name: Hickish, Jack
name: Josaitis, Alec
name: Julius, Austin
name: Igarashi, Amy S.
name: Kariseb, MacCalvin
name: Kohn, Saul A.
name: Kolopanis, Matthew
name: Lekalake, Telalo
name: Loots, Anita
name: MacMahon, David
name: Malan, Lourence
name: Malgas, Cresshim
name: Maree, Matthys
name: Martinot, Zachary E.
name: Mathison, Nathan
name: Matsetela, Eunice
name: Mesinger, Andrei
name: Neben, Abraham R.
name: Nikolic, Bojan
name: Nunhokee, Chuneeta D.
name: Patra, Nipanjana
name: Pieterse, Samantha
name: Razavi-Ghods, Nima
name: Ringuette, Jon
name: Robnett, James
name: Rosie, Kathryn
name: Sell, Raddwine
name: Smith, Craig
name: Syce, Angelo
name: Tegmark, Max
name: Thyagarajan, Nithyanandan
name: Williams, Peter K. G.
name: Zheng, Haoxuan
Publisher: name: arXiv
Year: 2020
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: 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.
description: 15 pages, 15 figures, 1 table, Accepted to MNRAS