The first black hole portrait has become sharper thanks to machine learning
If the first image of a black hole looked like a fuzzy donut, this one is a thin onion ring.
Using a machine learning technique, scientists have refined the portrait of the supermassive black hole at the center of galaxy M87, revealing a thinner halo of glowing gas than previously observed.
In 2019, scientists from the Event Horizon Telescope unveiled an image of M87’s black hole (SN: 04/10/19). The photo was the first ever taken of a black hole and showed a fuzzy orange ring of swirling gas silhouetted by the black juggernaut. THE the thickness of the new ring is half of that of the originalalthough based on the same data, the researchers report on April 13 in the Astrophysical Journal Letters.
The Event Horizon Telescope takes data using a network of telescopes around the world. But this technique leaves holes in the data. “Since we can’t just cover the whole Earth with telescopes, that means information is missing,” says astrophysicist Lia Medeiros of the Institute for Advanced Study in Princeton, NJ. “We need an algorithm that can fill in these gaps.
Previous analyzes had used certain assumptions to address these shortcomings, such as preferring a smooth image. But the new technique uses machine learning to fill in those gaps based on more than 30,000 simulated images of matter swirling around a black hole, creating a sharper image.
In the future, this technique could help scientists better understand the mass of the black hole and perform improved gravity tests and other studies of black hole physics.