Upgraded View of 'Fuzzy' Supermassive Black Hole is a Shade More Menacing

upstart writes:

Machine learning has boosted the resolution of an image produced by Event Horizon Telescope data captured six years ago:

Using machine learning, a team of researchers has enhanced the first image ever taken of a distant black hole. Importantly, the newly updated image shows the full resolution of the telescope array for the very first time.

[...] The machine learning model has sharpened the otherwise blurry image of black hole M87, showcasing the utility of machine learning models in improving radio telescope images. The team's research was published today in the Astrophysical Journal Letters.

"Approximately four years after the first horizon-scale image of a black hole was unveiled by EHT in 2019, we have marked another milestone, producing an image that utilizes the full resolution of the array for the first time," said Dimitrios Psaltis, a researcher at Georgia Tech and a member of the EHT collaboration, in an Institute for Advanced Study release. "The new machine learning techniques that we have developed provide a golden opportunity for our collective work to understand black hole physics."

[...] But even using radio telescopes around the world doesn't give astronomers a complete view of the black hole; by incorporating a machine learning technique called PRIMO, the collaboration was able to improve the array's resolution. What appeared a bulbous, orange doughnut in a 2019 image has now taken on the delicate, thin circle of The One Ring.

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