Data processing pipeline for Tianlai experiment

Zuo, Shifan; Li, Jixia; Li, Yichao; Santanu, Das; Stebbins, Albert; Masui, Kiyoshi W.; Shaw, Richard J.; Zhang, Jiao; Wu, Fengquan; Chen, Xuelei

doi:10.1016/j.ascom.2020.100439

DOE OSTI · 1632194

Data processing pipeline for Tianlai experiment

Zuo, Shifan·Li, Jixia·Li, Yichao·Santanu, Das·Stebbins, Albert·Masui, Kiyoshi W.·Shaw, Richard J.·Zhang, Jiao·Wu, Fengquan·Chen, Xuelei

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Abstract

The Tianlai project is a 21cm intensity mapping experiment for 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 21cm signal extraction, which is still a great challenge in current radio astronomy research. The 21cm signal is much weaker than the foregrounds and easily aected 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 called tlpipe to process the drift scan survey data from the Tianlai experiment. It performs oine data processing tasks such as radio frequency interference (RFI) agging, 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 Tikhnov regularization for m-mode analysis. In this paper we describe the design and implementation of the pipeline and illustrate its functions with some analysis of real data. Finally, we outline directions for future development of this publicly code.

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BibTeX RIS

Zuo, Shifan, Li, Jixia, Li, Yichao, Santanu, Das, Stebbins, Albert, Masui, Kiyoshi W., Shaw, Richard J., Zhang, Jiao, Wu, Fengquan, Chen, Xuelei. 2021-11-27. Data processing pipeline for Tianlai experiment. https://doi.org/10.1016/j.ascom.2020.100439

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