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

Autor: Max Tegmark, Aaron R. Parsons, Samavarti Gallardo, Angelo Syce, Jon Ringuette, Adam P. Beardsley, Gianni Bernardi, Richard F. Bradley, Matthew Kolopanis, Mario G. Santos, Adrian Liu, Kathryn Rosie, T. L. Grobler, Nicholas S. Kern, Brian Glendenning, Amy S. Igarashi, Siyanda Matika, Daniel C. Jacobs, Carina Cheng, Oleg Smirnov, Nithyanandan Thyagarajan, Haoxuan Zheng, Peter K. G. Williams, Matthys Maree, Roshan K. Benefo, Nathan Mathison, Lourence Malan, Austin Julius, Nima Razavi-Ghods, Cresshim Malgas, B. K. Gehlot, Nicolas Fagnoni, Bryna J. Hazelton, Andrei Mesinger, Chuneeta D. Nunhokee, Jasper Grobbelaar, David MacMahon, Deepthi Gorthi, Léon V. E. Koopmans, Joshua S. Dillon, Steve R. Furlanetto, Abraham R. Neben, Chris Carilli, Tashalee S. Billings, Zachary E. Martinot, Judd D. Bowman, Samantha Pieterse, Paul Alexander, Randall Fritz, James Robnett, Telalo Lekalake, Raddwine Sell, Saul A. Kohn, Eloy de Lera Acedo, Florent Mertens, Alec Josaitis, Bradley Greig, Nipanjana Patra, Craig Smith, Austin F. Fortino, David DeBoer, Miguel F. Morales, Zaki S. Ali, Bojan Nikolic, Aaron Ewall-Wice, Eunice Matsetela, MacCalvin Kariseb, Gcobisa Fadana, Paul M. Chichura, Jack Hickish, James E. Aguirre, Abhik Ghosh, Anita Loots
Přispěvatelé: Ghosh, A., Mertens, F., Bernardi, G., Santos, M. G., Kern, N. S., Carilli, C. L., Grobler, T. L., Koopmans, L. V. E., Jacobs, D. C., Liu, A., Parsons, A. R., Morales, M. F., Aguirre, J. E., Dillon, J. S., Hazelton, B. J., Smirnov, O. M., Gehlot, B. K., Matika, S., Alexander, P., Ali, Z. S., Beardsley, A. P., Benefo, R. K., Billings, T. S., Bowman, J. D., Bradley, R. F., Cheng, C., Chichura, P. M., Deboer, D. R., Acedo, E. D. L., Ewall-Wice, A., Fadana, G., Fagnoni, N., Fortino, A. F., Fritz, R., Furlanetto, S. R., Gallardo, S., Glendenning, B., Gorthi, D., Greig, B., Grobbelaar, J., Hickish, J., Josaitis, A., Julius, A., Igarashi, A. S., Kariseb, M., Kohn, S. A., Kolopanis, M., Lekalake, T., Loots, A., Macmahon, D., Malan, L., Malgas, C., Maree, M., Martinot, Z. E., Mathison, N., Matsetela, E., Mesinger, A., Neben, A. R., Nikolic, B., Nunhokee, C. D., Patra, N., Pieterse, S., Razavi-Ghods, N., Ringuette, J., Robnett, J., Rosie, K., Sell, R., Smith, C., Syce, A., Tegmark, M., Thyagarajan, N., Williams, P. K. G., Zheng, H., Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), ITA, USA, ZAF, Astronomy, Kapteyn Astronomical Institute
Rok vydání: 2020
Předmět:
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
interferometers [instrumentation]
first stars
statistical [methods]
FOS: Physical sciences
Astrophysics::Cosmology and Extragalactic Astrophysics
Astrophysics
Astronomy & Astrophysics
01 natural sciences
Signal
Settore FIS/05 - Astronomia e Astrofisica
0103 physical sciences
Coherence (signal processing)
dark ages
reionization
first stars

dark ages
instrumentation: interferometers
010306 general physics
010303 astronomy & astrophysics
Reionization
Astrophysics::Galaxy Astrophysics
Physics
methods: statistical
COSMIC cancer database
Astrophysics::Instrumentation and Methods for Astrophysics
Spectral density
Astronomy and Astrophysics
White noise
observations [cosmology]
Redshift
diffuse radiation
Periodic function
interferometer [instrumentation]
Space and Planetary Science
cosmology: observations
astro-ph.CO
reionization
dark ages
reionization
first star

large-scale structure of Universe
[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Astronomical and Space Sciences
Astrophysics - Cosmology and Nongalactic Astrophysics
observation [cosmology]
Zdroj: Monthly Notices of the Royal Astronomical Society, vol 495, iss 3
Mon.Not.Roy.Astron.Soc.
Mon.Not.Roy.Astron.Soc., 2020, 495 (3), pp.2813-2826. ⟨10.1093/mnras/staa1331⟩
Monthly Notices of the Royal Astronomical Society, 495(3), 2813-2826. Oxford University Press
arXiv
ISSN: 1365-2966
0035-8711
DOI: 10.1093/mnras/staa1331
Popis: 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.
15 pages, 15 figures, 1 table, Accepted to MNRAS
Databáze: OpenAIRE