Details

Autor(en) / Beteiligte

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

Titel

Foreground modelling via Gaussian process regression: an application to HERA data

Ist Teil von

• Monthly notices of the Royal Astronomical Society, 2020-07, Vol.495 (3), p.2813-2826

Ort / Verlag

Oxford University Press

Erscheinungsjahr

2020

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• 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 ∼2 h 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 and 2.4 MHz, respectively (dominating the line-of-sight power spectrum over scales k∥ ≤ 0.2 h cMpc−1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over k∥ ∼ 0.4–0.8 h cMpc−1), which should be distinguishable from the 21-cm Epoch of Reionization signal whose typical coherence scale is ∼0.8 MHz.