A neural network has been trained using millions of simulations of supermassive black holes, enabling it to analyze fuzzy data collected from real black holes. This advancement comes from an international team of astronomers and the Morgridge Research Institute in Wisconsin.
The Event Horizon Telescope (EHT), a network of radio telescopes working collectively across the globe, has previously yielded images of black holes like M87 and Sagittarius A*. These images are not traditional photographs but visualizations created from radio waves emitted by the black holes. The challenge lies in interpreting the data—significant amounts are discarded due to difficulties in analysis.
The newly trained neural network aims to better analyze this discarded data and enhance the resolution of EHT findings. According to the Morgridge Research Institute, the AI has reevaluated the characteristics of Sagittarius A*, the supermassive black hole at the center of the Milky Way, producing an alternative image that reveals previously unknown characteristics. Researchers propose that Sagittarius A* may be rotating at nearly maximum speed, which is a notable revision of previous estimates that classified its rotation as moderate to fast.
Understanding the rotational speed of Sagittarius A* is vital, as it affects the behavior of nearby radiation and offers insights into the stability of the black hole’s surrounding material. Lead researcher Michael Janssen, from Radboud University in the Netherlands, expressed excitement about defying existing theories but emphasized that this is just the beginning. The team plans to refine and broaden their models and simulations in future work.
For more about this research, you can check the full press release here.
