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The Discovery FilesReconstructed Black Hole Image
Astrophysicists have used a novel machine-learning algorithm to update the Event Horizon Telescope array image of the black hole at the center of Messier 87 to its full resolution.
Credit: U.S. National Science Foundation
Prior to a series of observations in the late 1960's and early 1970's, Black holes were widely considered a mathematical curiosity. The first images to confirm the existence of a black hole were revealed to the public in 2019. Recently, a new machine learning technique has given the now iconic orange donut a makeover. We'll explore new insights into black holes in the U.S. National Science Foundation's "Discovery Files."
Thanks to advancements in machine learning, we now have the clearest picture of a black hole to date. In 2017 the Event Horizon Telescope array recorded data of M87, a galaxy 55 million light years from earth with a supermassive black hole at its heart. Supported in part by NSF, a consortium of EHT members have developed a novel algorithm; principal-component interferometric modeling, otherwise known as PRIMO, which was trained on simulated black holes to be able to identify common patterns in the structure of the images.
The PRIMO algorithm was used on the original EHT observation data in a new approach to a landmark result, the full resolution of the Event Horizon telescope array and a sharper look at the M87 Black Hole.
The new image further exposes a central region that is larger and darker than previously known, surrounded by bright accreting gas.
The detail in the image plays a critical role in our ability to understand its behavior and the new features provide a powerful constraint for our theoretical models and tests of gravity.
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