The Celebration Horizon Telescope is the most well-known of the different methods for black gap investigation. Instead of being a single tool, the EHT is made up of several radio telescopes that function up. It’s been possible to obtain pictures of the supermassive black holes M87 and Sagittarius A* thanks to the EHT. These are radio waves coming from black holes, no standard images, but rather visuals.
Mainframes from various parts of the world processed the radio signals that the EHT collected to make these images. However, they also discarded a lot of the information that was gathered because it was challenging to perceive. The Morgridge Research Institute in Wisconsin’s new neural system aims to use that wealth of data to improve the accuracy of the EHT’s analyses and uncover novel insights.
The university claims in a press release that the artificial intelligence was able to identify the constellation Sagittarius A*, which is located at the Milky Way’s core, with its new parameters. A fresh structure-based image of the dark hole was created, resulting in some intriguing new features.
Researchers now believe that the dark opening at the Milky Way’s facility is spinning at “almost best speed,” according to a press release from the experts. The new photo also provides fresh information about the origins and characteristics of the material disks that move around the dark hole, as well as showing that the black hole’s movement plane points to Earth.
Astronomers had recently predicted that Sagittarius A* rotates at a reasonable to fast rate. Knowing its exact rotary speed is crucial because it enables us to infer how the rays around the dark hole behaves and provides information about its stability.
Direct researcher Michael Janssen of Radboud University Nijmegen in the Netherlands stated in the media launch,” That we are defying the prevailing idea is of course exciting.” ” But, I view our approach to AI and machine learning largely as a first step. Following, we will enhance and expand the concepts and models that are associated.
This article was first published on WIRED en Espaol and has since been translated into Spanish.
