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Residual Imager with Briggs Weighting #110

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iancze opened this issue Dec 14, 2022 · 2 comments · Fixed by #156
Closed

Residual Imager with Briggs Weighting #110

iancze opened this issue Dec 14, 2022 · 2 comments · Fixed by #156
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@iancze
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iancze commented Dec 14, 2022

Is your feature request related to a problem or opportunity? Please describe.
After #17 is implemented, it will be possible to make residual images using Briggs weighting. Currently, it is only possible to visualize residuals using uniform weighting, as the visibilities have been gridded. This isn't ideal, because it means that the residuals have very high thermal noise levels and therefore any structure is hard to recognize.

Describe the solution you'd like

  • Create an easy workflow that calculates loose residual visibilities and then uses the Dirty Imager to make a Briggs-weighted image.
  • Write a tutorial demonstrating this functionality, or add to an existing tutorial
@iancze
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iancze commented Feb 18, 2023

The NuFFT has now been implemented in #17, so it's possible to do this. Following discussion in #126 and #100 about reformatting Gridder and GriddedDataset, I think it would be best to make the following the primary way of imaging residuals:

  1. Use an RML loop with a GriddedDataset to converge to the MLE + regularized image
  2. Pass the image and the original u,v baselines through a NuFFT object to produce "loose" model visibilities
  3. Calculate residual visibilities as data - model
  4. Send those residual visibilities to the new version (Split Gridder averaging and imaging capabilities into different classes #154, Split averaging and imaging capabilities of Gridder into different classes. #156) of the DirtyImager routine, choose a Briggs weighting, and produce a dirty image of the residual visibilities

There are other routines around (like GriddedResidualConnector) that tried to accomplish visualization of residuals by just iFFT'ing the gridded residuals, but this does not allow any kind of data weighting other than uniform (which has the worst thermal sensitivity). Generally, good thermal sensitivity is useful when examining (faint) residual structures, so this was a problem. Depending on how efficient the proposed residual imaging loop ends up being, we might be able to delete GriddedResidualConnector from the codebase entirely.

@iancze
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iancze commented Feb 20, 2023

Closed by #158

@iancze iancze closed this as completed Feb 20, 2023
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