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  1. SAFe Program
  2. SP-1472

Facet-based imaging of SKA1-LOW simulated data.

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    • PI9
    • COM SDP SW
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    • Data Processing
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      To achieve high-quality imaging results with the SKA, we need to understand the trade-offs between, e.g., computational demands and image quality across the field-of-view for various available imaging approaches. Here we look into the impact of screen-based vs. facet-based DD correction during imaging.

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      To achieve high-quality imaging results with the SKA, we need to understand the trade-offs between, e.g., computational demands and image quality across the field-of-view for various available imaging approaches. Here we look into the impact of screen-based vs. facet-based DD correction during imaging.
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      • WSClean is augmented to support facet-based imaging
      • SKA-LOW simulated data with known A-terms are imaged using IDG
      • SKA-LOW simulated data with known a-terms are imaged using facet-based approach in WSClean
      • Report on comparison of the results
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      WSClean is augmented to support facet-based imaging SKA-LOW simulated data with known A-terms are imaged using IDG SKA-LOW simulated data with known a-terms are imaged using facet-based approach in WSClean Report on comparison of the results
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    • 5
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    • Team_SCHAAP
    • Sprint 5
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    • 10.4
    • Stories Completed, Integrated, Outcomes Reviewed, NFRS met, Demonstrated, Satisfies Acceptance Criteria, Accepted by FO

    • PI22 - UNCOVERED

    Description

      This ticket is a follow-up to SP-1233 capturing the work towards its stretch goals. In PI8, Image Domain Gridding (IDG) was used to apply screen-based corrections during imaging of SKA1-Low simulated data. Screen-based corrections assume that the DD gains are spatially smooth. Facet-based imagers, on the other hand, assume the DD gains to be piece-wise constant within each facet. We want to compare these two approaches using SKA-LOW simulated data with a priori known A-term, i.e., we want to compare the two imaging approaches without complicating factors like calibration (errors) and pre-processing, which can be investigated in future tickets.

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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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          relates to

          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-1233 Compare screen-based DD imaging with facet-based DD imaging for SKA1-LOW

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          • Done
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                D.Fenech Fenech, Danielle
                s.wijnholds Stefan Wijnholds
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                Feature Progress

                  Story Point Burn-up: (100.00%)

                  Feature Estimate: 5.0

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                  Complete1749.0
                  Total1749.0

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