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  1. SAFe Program
  2. SP-3328

Provide additional ionospheric calibration routines to better constrain SKA-Low phase screens at AA2+ scale

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    • Feature
    • Could have
    • PI18
    • COM SDP SW
    • None
    • Data Processing
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      The SKA-Low self-calibration pipeline has a mechanism for applying ionospheric phase screens to correct for ionospheric distortions, however constraining these screens from the data can be challenging. This feature will provide an additional means of generating phase screen constraints that should improve overall modelling.

      Show
      The SKA-Low self-calibration pipeline has a mechanism for applying ionospheric phase screens to correct for ionospheric distortions, however constraining these screens from the data can be challenging. This feature will provide an additional means of generating phase screen constraints that should improve overall modelling.
    • Hide

      The ska-sdp-func-python package is available and uses standard ska-sdp-datamodels data models.

      A demonstration of algorithm performance and a comparison against antenna-based phase calibration.

      Show
      The ska-sdp-func-python package is available and uses standard ska-sdp-datamodels data models. A demonstration of algorithm performance and a comparison against antenna-based phase calibration.
    • 3
    • 3
    • 0
    • Team_YANDA
    • Sprint 5
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      This feature adds further ionospheric calibration routines to better constrain phase screens. It improves upon current solvers by exploiting the clustering of stations to further constrain the ionosphere above the station apertures.

      This is demonstrated with notebooks that compare the clustered station method with the station only calibration schemes.

      The AC for this feature are 

      AC 1 The ska-sdp-func-python package is available and uses standard ska-sdp-datamodels data models.

      These are added by https://gitlab.com/ska-telescope/sdp/ska-sdp-func-python/-/merge_requests/33

      AC 2 A demonstration of algorithm performance and a comparison against antenna-based phase calibration.

      This is demonstrated using python notebooks attached to tickets:

       

      Show
      This feature adds further ionospheric calibration routines to better constrain phase screens. It improves upon current solvers by exploiting the clustering of stations to further constrain the ionosphere above the station apertures. This is demonstrated with notebooks that compare the clustered station method with the station only calibration schemes. The AC for this feature are  AC 1 The ska-sdp-func-python package is available and uses standard ska-sdp-datamodels data models. These are added by https://gitlab.com/ska-telescope/sdp/ska-sdp-func-python/-/merge_requests/33 AC 2 A demonstration of algorithm performance and a comparison against antenna-based phase calibration. This is demonstrated using python notebooks attached to tickets: yan-1288.ipynb Demonstrate solver with complex sky models yan-1312.ipynb Demonstrate solver in a peel loop yan-1326.ipynb  Compare to antenna based calibration    
    • 21.6
    • Stories Completed, Integrated, Outcomes Reviewed, Satisfies Acceptance Criteria, Accepted by FO
    • PI24 - UNCOVERED

    • SDP-G2

    Description

      Who: SDP pipeline developers

      What: A ska-sdp-func-python package that can supply the SKA-Low self-calibration pipeline (SP-3186) with additional phase shift measurements across the field of view, to better constrain ionospheric phase screens. This feature is contributing to AA2-scale development of the pipeline.

      Why: The software is an extension of a calibration approach used with MWA data, which was demonstrated in an earlier PI. The main difference compared to standard calibration is that it fits for low-order dTEC variations across the aperture (or across station clusters), instead of antenna-based phase shifts. While this may probe ionospheric delays across the array with lower spatial resolution than antenna-based parameters, the large increase in data points per model parameter means that fainter radio sources can be used as calibrators, increasing the sampling across the field of view. 

      Attachments

        1. Figure_1-large.png
          Figure_1-large.png
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        2. Figure_1-Sarm.png
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        3. Figure_1-small.png
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        4. Figure_3-large.png
          Figure_3-large.png
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        5. Figure_3-Sarm.png
          Figure_3-Sarm.png
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        6. Figure_3-small.png
          Figure_3-small.png
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        7. yan-1288.ipynb
          689 kB
        8. yan-1312.ipynb
          1.14 MB
        9. yan-1326.ipynb
          617 kB

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                D.Fenech Fenech, Danielle
                d.mitchell Mitchell, Daniel
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