Forward imaging is a direct data-domain approach in radio astronomy that solves images by exploiting source structure for super-resolution, offering flexible data and prior choices while incorporating complex mathematical effects, though it remains computationally intensive and currently lacks scalability for large arrays like ALMA, VLA, and SKA.
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Is Forward Imaging the Future of Radio Astronomy?Added:
Um, so I just want to summarize pros and cons of foreign imaging method. As I said, the inverse modeling approach is great. Um, but actually there are some pros in the on the foreign modeling imaging method. So it use, um, you know, measurement equation and solve images in a direct directly in a data domain. So generally it is actually have a good advantage to maximally exploit the source structure seen in the data. That sometimes it is a model super resolution. We have a flexible choice of data and prior assumptions.
Um, that allows also, you know, flexible imaging, um, and also my my going incorporate various effects in imaging.
And the Bayesian imaging method developed recently allows also more rigorous estimate and a direct inference of the posterior distribution, which is actually exciting for the NL.
I think the primary concept of the these method is a computationally expensive.
This was not in the 1970s because it's very just computationally expensive and we do not have, you know, minor cycles where we can solve images in a purely in the image domain.
Um, and also most of current foreign imaging packages are not yet scalable for general imaging for Alma or large arrays, in GBLA, VLA, SKA, for example.
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