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 |
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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 |
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