Here’s one …
https://www.digitaltrends.com/news/black-holes-scale/
But could this might have something to do with the methodology itself?Black holes all look like donuts, regardless of their size
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The black hole, called Sagittarius A*, is a type called a supermassive black hole, which is found at the center of almost all galaxies. Ours is on the smaller end for such giants: At 4.3 million times the mass of the sun, it’s much smaller than other monsters like the one is Messier 87 which was imaged in 2019 and which is 6.5 billion times the mass of the sun.
… snip …
However, images of these two black holes look notably similar, both showing a distinctive donut shape. And that agrees precisely with physicists’ predictions, which said that black holes would appear the same no matter what size they are.
As was pointed out years ago, these are not “images” snapped with a camera. It takes months and months of processing to *create* one and that processing is based on the assumption that it’s a black hole and that we sort of know what a black hole looks like. In other words, they pre-program the algorithms they are using to get donuts looking “images”. They even admit that in their article on the methodology. For example …
https://www.csail.mit.edu/news/method-image-black-holes
Even better, here’s a paper by Katherine L. Bouman (et. al.) essentially saying this …A method to image black holes
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and Harvard University have developed a new algorithm that could help astronomers produce the first image of a black hole.
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Even with atmospheric noise filtered out, the measurements from just a handful of telescopes scattered around the globe are pretty sparse; any number of possible images could fit the data equally well. So the next step is to assemble an image that both fits the data and meets certain expectations about what images look like.
https://arxiv.org/pdf/1512.01413.pdf
As the paper states …
… and …Reconstructing an image using bispectrum measurements is an ill-posed problem, and as such there are an infinite number of possible images that explain the data. The challenge is to find an explanation that respects our prior assumptions about the “visual” universe while still satisfying the observed data.
We generate data using a collection of black hole, celestial, and natural images.
… and …
Here’s another article discussing this …Flexibility of the patch prior framework allows us to easily incorporate a variety of different “visual” assumptions in our reconstructed image. For instance, in the case of the EHT, simulations of a black hole for different inclinations and spins can be used to train a patch model that can be subsequently used for reconstruction.
https://educationalblogspotforyou.wordp ... hole-html/
So, if they don’t put into the mix images that presuppose … say … a plasmoid (which I bet they didn’t), they might not ever get an *image* out of their methodology that matches a plasmoid. Or a plasmoid might look much the same as a black hole … in which case, they are again fooled into calling it a picture of a black hole only because they assumed it was a black hole.Algorithms developed to take the picture of the black hole.
Since there are number of infinite images that perfectly explain our telescope measurements, we have to chose between them in some way. We do this by ranking the images based upon how likely they are to be the black hole image,and then choosing the one that’s most likely.
But when it comes to the images from black hole, we’re posed with a real conundrum; we’ve not seen any black hole images before.
In that case what is likely a black hole image, and what should we assume about the structure of black hole?
If all images produce a very similar – looking image, then we can start to become more confident.
One way we can try to impose different image features is by using pieces of existing images. So, we take a large collection of images, and we break them down into their little patches. We then can treat each image like a puzzle pieces. And we used the commonly seen puzzle pieces to piece together in an image that also fits our telescopic measurements. Different types of pieces has distinctive set of puzzle pieces.
Just saying …