Foreground modelling via Gaussian process regression: an application to HERA data
Name
2004.06041.pdf
Description
Accepted version
Size
2.1 MB
Format
Unknown
Checksum (MD5)
a13cbd825609a23c7f4b2c76975485ba
Author(s)
Ewall-Wice, Aaron Michael
Neben, Abraham Richard
Tegmark, Max Erik
Zheng, Haoxuan
Date Issued
2020
Journal
Monthly Notices of the Royal Astronomical Society
Publisher
Oxford University Press (OUP)
Version
Author's final manuscript
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1093/MNRAS/STAA1331
