Data processing pipeline for Tianlai experiment
Name
2011.10757.pdf
Description
Accepted version
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Author(s) • • • • • • • • •
Zuo, S
Li, J
Li, Y
Santanu, D
Stebbins, A
Masui, KW
Shaw, R
Zhang, J
Wu, F
Chen, X
Date Issued
2021
Journal
Astronomy and Computing
Publisher
Elsevier BV
Citation
Zuo, S, Li, J, Li, Y, Santanu, D, Stebbins, A et al. 2021. "Data processing pipeline for Tianlai experiment." Astronomy and Computing, 34.
Version
Author's final manuscript
Abstract
© 2020 Elsevier B.V. The Tianlai project is a 21 cm intensity mapping experiment aimed at detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21 cm signal extraction, which is still a great challenge in current radio astronomy research. The 21 cm signal is much weaker than the foregrounds and easily affected by the imperfections in the instrumental responses. Furthermore, processing the large volumes of interferometer data poses a practical challenge. We have developed a data processing pipeline software called tlpipe to process the drift scan survey data from the Tianlai experiment. It performs offline data processing tasks such as radio frequency interference (RFI) flagging, array calibration, binning, and map-making, etc. It also includes utility functions needed for the data analysis, such as data selection, transformation, visualization and others. A number of new algorithms are implemented, for example the eigenvector decomposition method for array calibration and the Tikhonov regularization for m-mode analysis. In this paper we describe the design and implementation of the tlpipe and illustrate its functions with some analysis of real data. Finally, we outline directions for future development of this publicly code.
MIT Department
MIT Kavli Institute for Astrophysics and Space Research
Massachusetts Institute of Technology. Department of Physics
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/J.ASCOM.2020.100439