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  1. SAFe Program
  2. SP-1235

Improve direct estimation of screens for DD corrections

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    • Enabler
    • Not Assigned
    • PI9
    • COM SDP SW
    • None
    • Data Processing
    • Hide

      SKA needs to self-calibrate its observations to compensate for DDEs. The physics of the observation dictates that these DDEs are spatially smooth. Exploiting spatial smoothness will reduce the number of degrees of freedom in calibration, thereby mitigating calibration artefacts like ghosts.

      Show
      SKA needs to self-calibrate its observations to compensate for DDEs. The physics of the observation dictates that these DDEs are spatially smooth. Exploiting spatial smoothness will reduce the number of degrees of freedom in calibration, thereby mitigating calibration artefacts like ghosts.
    • Hide
      • Demonstrate channel-based solutions obtained by IDGCal on SKA-LOW simulated data.
      • (Stretch) Extend IDGCal with spectral smoothing (calibration over multiple channels).
      • (Stretch) Onboard a script to fit TEC screens (TEC and delay) to DD gain solutions across multiple frequencies. 
      Show
      Demonstrate channel-based solutions obtained by IDGCal on SKA-LOW simulated data. (Stretch) Extend IDGCal with spectral smoothing (calibration over multiple channels). (Stretch) Onboard a script to fit TEC screens (TEC and delay) to DD gain solutions across multiple frequencies. 
    • 4
    • 4
    • 8.25
    • Team_SCHAAP
    • Sprint 5
    • Hide
      • Direct estimation of screens was demonstrated during System Demo 9.6 (slides have been uploaded to the Confluence page).
      • For this demonstration, we used a LOFAR data set as some (time-consuming) issue were encountered while reproducing the SKA-LOW simulations (needed to retrieve the ground truth to compare against).
      • Due to several delays during the PI, we did not get to the stretch goals of this ticket.
      Show
      Direct estimation of screens was demonstrated during System Demo 9.6 (slides have been uploaded to the Confluence page). For this demonstration, we used a LOFAR data set as some (time-consuming) issue were encountered while reproducing the SKA-LOW simulations (needed to retrieve the ground truth to compare against). Due to several delays during the PI, we did not get to the stretch goals of this ticket.
    • 10.4
    • Stories Completed, Integrated, Outcomes Reviewed, NFRS met, Demonstrated, Satisfies Acceptance Criteria, Accepted by FO

    • PI24 - UNCOVERED

    • SPO-880

    Description

      In DD imaging, there are two distinct approaches to DD corrections: screen-based and facet-based. In calibration, screens are usually derived from DD gains in discrete directions. IDGCal attempts to solve for screens (spatially smooth DD gains) directly. We want to

      1. demonstrate that direct estimation of screens is possible on a per frequency channel basis and
      2. extend IDGCal with spectral smoothing of solutions.

      This will allow us to compare a screen-based approach to calibration with a facet-based approach to calibration in a future PI.

      As a stretch goal of this ticket, we want to onboard a script that fits screens to DD calibration solutions obtained for discrete directions.

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          relates to

          Spike - A time limited investigation to get some knowledge SP-1326 Determine initial design for a continuum pipeline and datasets

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          • Done

          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-1539 Connect beam software EveryBeam to Yandasoft

          • Not Assigned - Priority is not assigned yet.
          • Done

          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-1746 Use EveryBeam library within Yandasoft

          • 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.
          • Discarded

          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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          • Done

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

                  Story Point Burn-up: (100.00%)

                  Feature Estimate: 4.0

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                  Complete1127.0
                  Total1127.0

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