Data processing pipeline for Tianlai experiment
© 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 cos...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Elsevier BV
2022
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| Online Access: | https://hdl.handle.net/1721.1/142154 |
| _version_ | 1826202376877375488 |
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| author | Zuo, S Li, J Li, Y Santanu, D Stebbins, A Masui, KW Shaw, R Zhang, J Wu, F Chen, X |
| author2 | MIT Kavli Institute for Astrophysics and Space Research |
| author_facet | MIT Kavli Institute for Astrophysics and Space Research Zuo, S Li, J Li, Y Santanu, D Stebbins, A Masui, KW Shaw, R Zhang, J Wu, F Chen, X |
| author_sort | Zuo, S |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T12:06:29Z |
| format | Article |
| id | mit-1721.1/142154 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:06:29Z |
| publishDate | 2022 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1421542023-07-05T20:48:47Z Data processing pipeline for Tianlai experiment Zuo, S Li, J Li, Y Santanu, D Stebbins, A Masui, KW Shaw, R Zhang, J Wu, F Chen, X MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology. Department of Physics © 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. 2022-04-27T17:55:00Z 2022-04-27T17:55:00Z 2021 2022-04-27T17:49:44Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142154 Zuo, S, Li, J, Li, Y, Santanu, D, Stebbins, A et al. 2021. "Data processing pipeline for Tianlai experiment." Astronomy and Computing, 34. en 10.1016/J.ASCOM.2020.100439 Astronomy and Computing Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV arXiv |
| spellingShingle | Zuo, S Li, J Li, Y Santanu, D Stebbins, A Masui, KW Shaw, R Zhang, J Wu, F Chen, X Data processing pipeline for Tianlai experiment |
| title | Data processing pipeline for Tianlai experiment |
| title_full | Data processing pipeline for Tianlai experiment |
| title_fullStr | Data processing pipeline for Tianlai experiment |
| title_full_unstemmed | Data processing pipeline for Tianlai experiment |
| title_short | Data processing pipeline for Tianlai experiment |
| title_sort | data processing pipeline for tianlai experiment |
| url | https://hdl.handle.net/1721.1/142154 |
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