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1826202809140248576
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MIT
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© 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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 kâ ≤ 0.2 h cMpc-1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over kâ ∼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.
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2024-09-23T12:19:36Z
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Article
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mit-1721.1/132378
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| institution |
Massachusetts Institute of Technology
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| language |
English
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2024-09-23T12:19:36Z
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| publishDate |
2021
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| publisher |
Oxford University Press (OUP)
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dspace
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| spelling |
mit-1721.1/1323782022-04-01T17:27:47Z Foreground modelling via Gaussian process regression: an application to HERA data © 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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 kâ ≤ 0.2 h cMpc-1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over kâ ∼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. 2021-09-20T18:22:07Z 2021-09-20T18:22:07Z 2020-11-09T19:34:29Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/132378 en 10.1093/MNRAS/STAA1331 Monthly Notices of the Royal Astronomical Society Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press (OUP) arXiv
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| spellingShingle |
Foreground modelling via Gaussian process regression: an application to HERA data
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| title |
Foreground modelling via Gaussian process regression: an application to HERA data
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| title_full |
Foreground modelling via Gaussian process regression: an application to HERA data
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| title_fullStr |
Foreground modelling via Gaussian process regression: an application to HERA data
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| title_full_unstemmed |
Foreground modelling via Gaussian process regression: an application to HERA data
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| title_short |
Foreground modelling via Gaussian process regression: an application to HERA data
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| title_sort |
foreground modelling via gaussian process regression an application to hera data
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| url |
https://hdl.handle.net/1721.1/132378
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