[wenc]

I believe in effect that is what they do in execution.

The 2D plot is more for training -- a human physicist picks out visual artifacts of interest to bootstrap the training. Humans of course can see blips and weird curves better on a visualization than in a pure data series.

For instance, a human can say if this little tail off a contour bends this way, it's right; if it bends in a different way, it's wrong. Or if an contour is "prickly" or "blobby".

Whether something "looks right" or "wrong" is really hard to mathematically reduce to a parsimonious description, especially when there's variance in the samples -- after all, there could be multiple subtle descriptions of "looks right" or "looks wrong" -- but a CNN is perfect for generalizing based on labeled samples. Similar to a radiologist looking at a scan and toggling isTumor = (True, False).