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The first photo ever taken of a black hole looks a little sharper now.
Originally released in 2019, the unprecedented historic image of the supermassive black hole at the center of the galaxy Messier 87 captured an essentially invisible celestial object using direct imaging.
The image presented the first direct visual evidence that black holes exist, showcasing a central dark region encapsulated by a ring of light that looks brighter on one side. Astronomers nicknamed the object the “fuzzy, orange donut.”
Now, scientists have used machine learning to give the image a cleaner upgrade that looks more like a “skinny” doughnut, researchers said. The central region is darker and larger, surrounded by a bright ring as hot gas falls into the black hole in the new image.
In 2017, astronomers set out to observe the invisible heart of the massive galaxy Messier 87, or M87, near the Virgo galaxy cluster 55 million light-years from Earth.
The Event Horizon Telescope Collaboration, called EHT, is a global network of telescopes that captured the first photograph of a black hole. More than 200 researchers worked on the project for more than a decade. The project was named for the event horizon, the proposed boundary around a black hole that represents the point of no return where no light or radiation can escape.
To capture an image of the black hole, scientists combined the power of seven radio telescopes around the world using Very-Long-Baseline-Interferometry, according to the European Southern Observatory, which is part of the EHT. This array effectively created a virtual telescope around the same size as Earth.
‘Maximum resolution’ achieved
Data from the original 2017 observation was combined with a machine learning technique to capture the full resolution of what the telescopes saw for the first time. The new, more detailed image, along with a study, was released on Thursday in The Astrophysical Journal Letters.
“With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” said lead study author Lia Medeiros, astrophysics postdoctoral fellow in the School of Natural Sciences at the Institute for Advanced Study in Princeton, New Jersey, in a statement.
“Since we cannot study black holes up-close, the detail of an image plays a critical role in our ability to understand its behavior. The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity.”
Medeiros and other EHT members developed Principal-component Interferometric Modeling, or PRIMO. The algorithm relies on dictionary learning in which computers create rules based on large amounts of material. If a computer is given a series of images of different bananas, combined with some training, it might be able to tell if an unknown image does or doesn’t contain a banana.
Computers using PRIMO analyzed more than 30,000 high-resolution simulated images of black holes to pick out common structural details. This allowed the machine learning essentially to fill in the gaps of the original image.
“PRIMO is a new approach to the difficult task of constructing images from EHT observations,” said Tod Lauer, an astronomer at the National Science Foundation’s National Optical-Infrared Astronomy Research Laboratory, or NOIRLab. “It provides a way to compensate for the missing information about the object being observed, which is required to generate the image that would have been seen using a single gigantic radio telescope the size of the Earth.”
Advancing research of black holes
Black holes are made up of huge amounts of matter squeezed into a small area, according to NASA, creating a massive gravitational field that draws in everything around it, including light. These powerful celestial phenomena also have a way of superheating the material around them and warping space-time.
Material accumulates around black holes, is heated to billions of degrees and reaches nearly the speed of light. Light bends around the gravity of the black hole, which creates the photon ring seen in the image. The black hole’s shadow is represented by the dark central region.
The visual confirmation of black holes also acts as confirmation of Albert Einstein’s theory of general relativity. In the theory, Einstein predicted that dense, compact regions of space would have such intense gravity that nothing could escape them. But if heated materials in the form of plasma surround the black hole and emit light, the event horizon could be visible.
The new image can help scientists make more accurate measurements of the black hole’s mass. Researchers can also apply PRIMO to other EHT observations, including those of the black hole at the center of our Milky Way galaxy.
“The 2019 image was just the beginning,” Medeiros said. “If a picture is worth a thousand words, the data underlying that image have many more stories to tell. PRIMO will continue to be a critical tool in extracting such insights.”