Deep spectral line imaging is one of the greatest challenges for the SKA and its pathfinders, as the raw visibility data volumes are too large to store long term, and the current imaging approach of combining individual images could 'freeze-in' low-level systematic artefacts. In this thesis, we present an alternative imaging approach by storing and combining gridded interferometric visibility data. The gridded data is sparse, lowering storage costs, while still enabling post-survey preconditioning, flagging and calibration. We demonstrate that our method creates higher fidelity deep images than the current imaging approach, using simulations and pilot observations from the DINGO survey.
|Qualification||Doctor of Philosophy|
|Award date||18 Nov 2021|
|Publication status||Unpublished - 2021|