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  1. SAFe Program
  2. SP-1236

Implement screen-based visibility prediction

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      Screen-based visibility prediction potentially provides computational advantages when DD correction is needed for many different directions and better prediction of diffuse emission. Both advantages are also useful for simulating realistic visibilities for fields with a lot of diffuse (foreground) emission. 

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      Screen-based visibility prediction potentially provides computational advantages when DD correction is needed for many different directions and better prediction of diffuse emission. Both advantages are also useful for simulating realistic visibilities for fields with a lot of diffuse (foreground) emission. 
      • Demonstrate screen-based visibility prediction based on Image Domain Gridding
    • 5
    • 5
    • 5
    • Team_SCHAAP
    • Sprint 5
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      • DPPP can use IDG for the entire field or individual directions for screen-based visibility prediction.
      • DPPP can combine screen based visibility prediction for some directions with direct prediction or model visibility data for other directions.
      Show
      DPPP can use IDG for the entire field or individual directions for screen-based visibility prediction. DPPP can combine screen based visibility prediction for some directions with direct prediction or model visibility data for other directions.
    • 14.2
    • Stories Completed, Integrated, BDD Testing Passes (no errors), Outcomes Reviewed, NFRS met, Demonstrated, Satisfies Acceptance Criteria, Accepted by FO

    • PI22 - UNCOVERED

    • SPO-708

    Description

      Direction-dependent effects (DDEs) are intrinsically smooth. However, currently, visibility prediction is done based on DD gains in discrete directions. We want to be able to apply screen-based corrections for spatially smooth DDEs during visibility prediction. This also puts us in a position to extend the DD imaging comparison described in SP-1233 to calibration.

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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-1003 Analysis of state-of-the-art DDE algorithms with SKA1 simulated data

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                b.mort Mort, Ben
                s.wijnholds Stefan Wijnholds
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                  Story Point Burn-up: (100.00%)

                  Feature Estimate: 5.0

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