Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-24T09:56:46.745Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust Foregrounds Removal for 21-cm Experiments

Published online by Cambridge University Press:  08 May 2018

F. Mertens Open the ORCID record for F. Mertens [Opens in a new window] ,
A. Ghosh  and
L. V. E. Koopmans
F. Mertens
Affiliation:
Kapteyn Astronomical Institute, University of Groningen, P. O. Box 800, 9700 AV Groningen, The Netherlands
A. Ghosh
Affiliation:
Department of Physics and Astronomy, University of the Western Cape, Robert Sobukwe Road, Bellville 7535, South Africa Square Kilometre Array radio telescope (SKA) South Africa, The Park, Park Road, Cape Town 7405, South Africa
L. V. E. Koopmans
Affiliation:
Kapteyn Astronomical Institute, University of Groningen, P. O. Box 800, 9700 AV Groningen, The Netherlands
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Direct detection of the Epoch of Reionization via the redshifted 21-cm line will have unprecedented implications on the study of structure formation in the early Universe. To fulfill this promise current and future 21-cm experiments will need to detect the weak 21-cm signal over foregrounds several order of magnitude greater. This requires accurate modeling of the galactic and extragalactic emission and of its contaminants due to instrument chromaticity, ionosphere and imperfect calibration. To solve for this complex modeling, we propose a new method based on Gaussian Process Regression (GPR) which is able to cleanly separate the cosmological signal from most of the foregrounds contaminants. We also propose a new imaging method based on a maximum likelihood framework which solves for the interferometric equation directly on the sphere. Using this method, chromatic effects causing the so-called “wedge” are effectively eliminated (i.e. deconvolved) in the cylindrical (k⊥, k∥) power spectrum.

Keywords

methods:data analysisstatistical; techniques:interferometric; cosmology: observationsearly universelarge-scale structure of universe
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S333: Peering towards Cosmic Dawn , October 2017 , pp. 284 - 287
Copyright
Copyright © International Astronomical Union 2018 

References

Ali, Z. S., et al. 2015, ApJ, 809, 61CrossRefGoogle Scholar
Beardsley, A. P., et al. 2016, ApJ, 833, 102CrossRefGoogle Scholar
Carozzi, T. D., 2015, MNRAS 451, L6Google Scholar
Chapman, E., et al. 2013, MNRAS, 429, 165CrossRefGoogle Scholar
Ewall-Wice, A., Dillon, J. S., Liu, A. & Hewitt, J., 2017, MNRAS, 470, 1849Google Scholar
Gehlot, B. K., et al. 2017, preprint, (arXiv: 1709.07727)Google Scholar
Ghosh, A., Mertens, F. G. & Koopmans, L. V. E., 2018, MNRAS, 474, 4552Google Scholar
Hazelton, B. J., Morales, M. F. & Sullivan, I. S., 2013, ApJ, 770, 156Google Scholar
Jelić, V., et al. 2008, MNRAS, 389, 1319Google Scholar
Koopmans, L. V. E., 2010, ApJ, 718, 963Google Scholar
Mertens, F. G., Ghosh, A. & Koopmans, L. V. E., 2017, preprint, (arXiv: 1711.10834)Google Scholar
Mesinger, A., Furlanetto, S. & Cen, R., 2011, MNRAS, 411, 955CrossRefGoogle Scholar
Patil, A. H., et al. 2016, MNRAS, 463, 4317Google Scholar
Patil, A. H., et al. 2017, ApJ, 838, 65Google Scholar
Rasmussen, C. E. & Williams, C. K. I., 2005, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT PressGoogle Scholar
Thyagarajan, N., et al. 2015, ApJ, 804, 14CrossRefGoogle Scholar
Vedantham, H., Udaya Shankar, N., & Subrahmanyan, R., 2012, ApJ, 752, 137Google Scholar