(I haven't watched the video, but I do have professional expertise on this topic.) With interferometry, you're getting an incomplete sampling of the Fourier tr

(I haven't watched the video, but I do have professional expertise on this topic.) With interferometry, you're getting an incomplete sampling of the Fourier transform of the sky image, and if you just invert the samples, you get what we call a "dirty" image. But you know your sampling of the Fourier plane exactly, since that's just a function of the projected baselines between every pair of telescopes during the observation, so you can create a "dirty beam" - now all you have to do is remove the effects of the dirty beam from the dirty image. Of course, that's a deconvolution problem, and given that you don't have all the information - you sampled it - it can never be exact. But it can be very good! There are very sophisticated radio synthesis image deconvolution algorithms, including CLEAN and Maximum Entropy. For Maximum Entropy methods, you can apply a Bayesian prior on your images - most of the time, the prior we apply is a blank sky (seriously!) but if we have other constraints that we can use (e.g., the approximate size of the region with extended emission), Bayes tells us that we would be remiss not to use it. If you look at this image [1] from Paper IV [2], we show the image results from different techniques on different observing days. Those are the inputs to what is the "consensus image" - you can check how close they all are to each other. Does that make sense...? [1] https://iopscience-event-horizon.s3.amazonaws.com/2041-8205/875/1/L4/downloadFigure/figure/apjlab0e85f11_lr.jpg https://iopscience-event-horizon.s3.amazonaws.com/2041-8205/... [2] https://iopscience.iop.org/article/10.3847/2041-8213/ab0e85 https://iopscience.iop.org/article/10.3847/2041-8213/ab0e85