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  1. SAFe Program
  2. SP-1489

Determine source model complexity favouring IDGpredict over direct predict

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    • Enabler
    • Won't have (this time)
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    • COM SDP SW
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    • Data Processing
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      IDGpredict is likely to be computationally more attractive than direct prediction for complex source models. The complexity of the source model can be measured in terms of the number of source components (point sources, Gaussian blobs) that are fed to direct prediction. Knowing the complexity of the source model at which IDGpredict becomes computationally more attractive than direct predict for the SKA case will help to inform further development of processing functions for visibility prediction.

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      IDGpredict is likely to be computationally more attractive than direct prediction for complex source models. The complexity of the source model can be measured in terms of the number of source components (point sources, Gaussian blobs) that are fed to direct prediction. Knowing the complexity of the source model at which IDGpredict becomes computationally more attractive than direct predict for the SKA case will help to inform further development of processing functions for visibility prediction.
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      • Several source models of increasing complexity are defined.
      • The time spent in predict for these source models is recorded for both IDGpredict and direction prediction for the SKA-Low configuration.
      • The time spent in predict for these source models is recorded for both IDGpredict and direction prediction for the SKA-Mid configuration.
      • The results are presented during a demo and summarised on a Confluence page.
      Show
      Several source models of increasing complexity are defined. The time spent in predict for these source models is recorded for both IDGpredict and direction prediction for the SKA-Low configuration. The time spent in predict for these source models is recorded for both IDGpredict and direction prediction for the SKA-Mid configuration. The results are presented during a demo and summarised on a Confluence page.
    • 2

    • PI22 - UNCOVERED

    • SPO-1002

    Description

      Visibility prediction is one of the most time consuming steps in the self-calibration loop, in particular when a complex source model (many source components, diffuse emission) is involved. Prediction using Image Domain Gridding (IDG), IDGpredict, could be a computationally attractive alternative to direct prediction. However, at the moment, experience from LOFAR indicates that the crossover point at which IDGpredict computationally outperforms direct prediction lies at several thousands of source components. As system architect, I want to know where the crossover point will be for the SKA-Low and SKA-Mid arrays is (expressed in number of source components in the source model), so that I can make an informed decision on further development of processing functions for visibility prediction.

       

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          Enabler - An Enabler supports the activities needed to extend the Architectural Runway to provide future business functionality. These include exploration, architecture, infrastructure, and compliance. SP-1904 Continued development of continuum imaging pipelines using LOFAR codes (WSClean, DP3, ...)

          • Should have - Items labelled as "Should have" are important but not necessary for delivery in the current delivery timebox. While "Should have" items can be as important as "Must have", they are often not as time-critical or there may be another way to satisfy the requirement so that it can be held back until a future delivery timebox.
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          Feature - A Feature is a service that fulfills a stakeholder need. Each feature includes a benefit hypothesis and acceptance criteria, and is sized or split as necessary to be delivered by a single Agile Release Train (ART) in a Program Increment (PI). SP-1331 Initial implementation of (continuum) imaging pipeline(s)

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                p.wortmann Wortmann, Peter
                s.wijnholds Stefan Wijnholds
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