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Comment by bovermyer | Hacker News Reader

The purpose of photography in science is to record observations.

Using AI to "clean up" the image is doctoring data to match expectations, which is bad science.

> We present a new reconstruction of the Event Horizon Telescope (EHT) image of the M87 black hole from the 2017 data set. We use PRIMO, a novel dictionary-learning-based algorithm that uses high-fidelity simulations of accreting black holes as a training set. By learning the correlations between the different regions of the space of interferometric data, this approach allows us to recover high-fidelity images even in the presence of sparse coverage and reach the nominal resolution of the EHT array. The black hole image comprises a thin bright ring with a diameter of 41.5 ± 0.6 μas and a fractional width that is at least a factor of 2 smaller than previously reported. This improvement has important implications for measuring the mass of the central black hole in M87 based on the EHT images

...«from the 2017 _data set_»

Edit, for more clarity:

> clean up

The image is reconstructed (not modified). Rebuilt. They used a different interpreter of the data.

Edit2: ...although there has been some amount of "gap-filling" ("best guessing").

No. This is not the conventional opinion in imaging science. We use mathematical models to improve image quality all the time.

Then I'm in error. Thank you for the correction.

I assume that the scientists are using the raw data directly, the probably aren’t even bothering to convert it to a format that is visible on a monitor. Same way this AI touch up is almost certainly just to give the public something to see

The point of the Event Horizon Telescope (ETH) was actually to attempt to produce something like an image of the black hole at the center of the galaxy, so in a sense it was a pretty-picture mission. The point of this study was to attempt to link up the observations with models in "picture-space".

There are certainly other, less pretty, ways to looks at the data, but producing something that can be intuitively understood, such as a 2D image, can also be helpful scientifically.

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