That means that the new image better conforms to the simulated data. I understand why it makes sense to do this (simulated data generated following the laws of physics, etc.) but it seems to me that the original point of the EHT was to experimentally verify that black holes actually look as we've modeled them for decades, so sharpening images using modeled data seems somewhat backwards.
However, I have zero subject matter expertise with physics, so my intuition is probably not worth much.
This concept seems to be worryingly lost in the flurry of excitement at using ML/AI in academic research by some.
They are just starting data collection at a higher frequency band that will allow higher resolution. Unfortunately our baseline is currently limited by the diameter of the earth so the only way to get sharper data is by using shorter wavelengths.
Is there a way to use the diameter of the earth "at this point in the orbit" to effectively make it into the diameter of the earth's orbit?
If we were to allow the Earth to rotate around the Sun and measure components of the same baseline at different times, we would violate this.
I don't know what exactly the tradoffs are, but I suspect this approach has a lower sensitivity due to size of the dishes, it's more difficult to get enough telescopes to form a good image, and transmitting the data back is likely to be a challenge (the black hole observations were shipped on hard drives instead of transmitted via the internet. Even achieving a broadband-speed transmission rate with a deep space object is difficult)
My first question here was how do we know the results are not 'hallucinated' or infilled by the ML system?
The fact that it was fed simulated data makes this really circular. If the goal is to get hard real-world data to validate the simulations, then training the machine that generates an "improved" data set on the simulation data itself would greatly increase the likelihood that the results will match the inputs, but not because that is what is actually out in reality.
Obviously, the people doing this are vastly more expert than I am, but it'd be good to see an explanation of how they are avoiding this fundamental issue.