Researchers at NOIRLab have released a version of the first image ever captured of a black hole that has been sharpened by using artificial intelligence. Photo courtesy of NOIRLab/Press Release
April 13 (UPI) -- Researchers at the National Science Foundation's NOIRLab have applied AI technology to the first image ever captured of a black hole to present a clearer image of how gas spirals into supermassive black holes.
When gas approaches a black hole, it swirls quickly and superheats because of friction, which in turn releases radiation that can be detected by radio telescopes.
The team published a paper in The Astrophysical Journal Letters detailing the process they used to sharpen the image.
The image of the supermassive black hole at the center of the Messier 87 Galaxy, which is approximately 54 million light years from Earth, was captured in 2017 by the Event Horizon Telescope collaboration.
The image was captured by an array of radio telescopes that create what researchers describe as an "Earth-sized interferometer." The team of researchers released the image in 2019.
"The image shows a bright ring formed as light bends in the intense gravity around a black hole that is 6.5 billion times more massive than the sun," EHT officials announced on Twitter at the time.
The sharpened image was created by running more than 30,000 images through computers using the principal-component interferometric modeling machine-learning system (PRIMO) to enhance the sharpness and fidelity of the Messier 87 image. Each individual image showed the process of gas accreting into a black hole.
"With our new machine-learning technique, PRIMO, we were able to achieve the maximum resolution of the current array," said the study's lead author Lia Medeiros.
Researchers hope the clearer image will help create a more accurate picture of how black holes function and give a more accurate representation of the mass of the supermassive black hole.
"Since we cannot study black holes up close, the detail in an images plays a critical role in our ability to understand its behavior. The width of the ring in the image is now smaller by a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity," Medeiros said.