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

Author:

Ghosh Abhik123ORCID,Mertens Florent45ORCID,Bernardi Gianni267,Santos Mário G12,Kern Nicholas S8,Carilli Christopher L910,Grobler Trienko L711,Koopmans Léon V E4,Jacobs Daniel C12,Liu Adrian813,Parsons Aaron R8,Morales Miguel F14,Aguirre James E15,Dillon Joshua S8,Hazelton Bryna J1416,Smirnov Oleg M27,Gehlot Bharat K412,Matika Siyanda7,Alexander Paul10,Ali Zaki S8,Beardsley Adam P12,Benefo Roshan K17,Billings Tashalee S15,Bowman Judd D12,Bradley Richard F18,Cheng Carina8,Chichura Paul M15,DeBoer David R8,Acedo Eloy de Lera10,Ewall-Wice Aaron19,Fadana Gcobisa2,Fagnoni Nicolas10,Fortino Austin F15,Fritz Randall2,Furlanetto Steve R20,Gallardo Samavarti1521,Glendenning Brian9,Gorthi Deepthi8,Greig Bradley2223,Grobbelaar Jasper2,Hickish Jack8,Josaitis Alec10,Julius Austin2,Igarashi Amy S1724,Kariseb MacCalvin2,Kohn Saul A15,Kolopanis Matthew12,Lekalake Telalo2,Loots Anita2,MacMahon David8,Malan Lourence2,Malgas Cresshim2,Maree Matthys2,Martinot Zachary E15,Mathison Nathan2,Matsetela Eunice2,Mesinger Andrei25,Neben Abraham R19,Nikolic Bojan10,Nunhokee Chuneeta D715,Patra Nipanjana8,Pieterse Samantha2,Razavi-Ghods Nima10,Ringuette Jon15,Robnett James9,Rosie Kathryn2,Sell Raddwine2,Smith Craig2,Syce Angelo2,Tegmark Max19,Thyagarajan Nithyanandan912,Williams Peter K G2627,Zheng Haoxuan19

Affiliation:

1. Department of Physics and Astronomy, University of Western Cape, Cape Town 7535, South Africa

2. The South African Radio Astronomy Observatory (SARAO), 2 Fir Street, Black River Park, Observatory, Cape Town 7925, South Africa

3. Department of Physics, Banwarilal Bhalotia College, GT Rd, Ushagram, Asansol, West Bengal 713303, India

4. Kapteyn Astronomical Institute, University of Groningen, PO Box 800, NL-9700 AV Groningen, the Netherlands

5. LERMA, Observatoire de Paris, PSL Research University, CNRS, Sorbonne Université, F-75014 Paris, France

6. INAF - IRA, via P. Gobetti 101, I-40129 Bologna, Italy

7. Department of Physics and Electronics, Rhodes University, PO Box 94, Grahamstown 6140, South Africa

8. Department of Astronomy, University of California, Berkeley, CA 94720, USA

9. National Radio Astronomy Observatory, Socorro, NM 87801, USA

10. Cavendish Astrophysics, University of Cambridge, CB3 0HE Cambridge, UK

11. Department of Mathematical Sciences, Computer Science Division, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa

12. School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287, USA

13. Department of Physics and McGill Space Institute, McGill University, 3600 University Street, Montreal, QC H3A 2T8, Canada

14. Department of Physics, University of Washington, Seattle, WA 98105, USA

15. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA

16. eScience Institute, University of Washington, Seattle, WA 98195, USA

17. Department of Physics and Astronomy, Center for Particle Cosmology, University of Pennsylvania, Philadelphia, PA 19104, USA

18. National Radio Astronomy Observatory, Charlottesville, VA 22903, USA

19. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02142, USA

20. Department of Physics and Astronomy, University of California, Los Angeles, CA 90095, USA

21. California State University of Los Angeles, 5151 State University Dr, Los Angeles, CA 90032, USA

22. School of Physics, University of Melbourne, Parkville, VIC 3010, Australia

23. ARC Centre of Excellence for All-Sky Astrophysics in 3 Dimensions (ASTRO 3D), University of Melbourne, VIC 3010, Australia

24. Department of Astronomy, San Diego State University, San Diego, CA 92182, USA

25. Scuola Normale Superiore, I-56126 Pisa, PI, Italy

26. Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA 02138, USA

27. American Astronomical Society, Washington, DC 20006, USA

Abstract

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.

Funder

National Science Foundation

Hawaii Educational Research Association

Gordon and Betty Moore Foundation

National Research Foundation

American Society for Radiation Oncology

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Neurosciences Research Foundation

Royal Society

Newton Fund

Institut sur la Nutrition et les Aliments Fonctionnels

Ministero degli Affari Esteri e della Cooperazione Internazionale

Natural Sciences and Engineering Research Council of Canada

Canadian Institute for Advanced Research

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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