The gigantic black hole located at the heart of the Milky Way is rotating at a speed close to the theoretical limit allowed by physics.
New Analytical Techniques Reveal Deeper Insights
This revelation is one of several insights uncovered by astrophysicists who developed a new technique to analyze data from the Event Horizon Telescope (EHT), which has revolutionized our understanding of black holes.
The EHT, a remarkable international collaboration, has provided humanity with its first direct glimpses of black hole shadows—first in M87*, a galaxy 55 million light-years away, and later in Sagittarius A* (Sgr A*), the supermassive black hole anchoring our own galaxy.
While the resulting images are groundbreaking, interpreting them poses significant challenges. To decode the visuals, researchers rely on advanced simulations. They generate a wide range of theoretical models and determine which ones best align with the observational data. This method has been widely used with EHT findings, but a recent advancement has taken it further.
Led by astronomer Michael Janssen from Radboud University and the Max Planck Institute for Radio Astronomy, the research team utilized high-performance computing to simulate millions of black hole configurations.
They then trained a neural network on this dataset, enabling it to extract detailed characteristics from the observational data and reveal the physical traits of the black holes in question.
Their analysis indicates that Sgr A* is spinning at a rate close to the maximum allowed by general relativity. Additionally, its spin axis appears to be directed toward Earth, and its surrounding glow is caused by superheated electrons.
Interestingly, the magnetic behavior of the material surrounding Sgr A* doesn’t align with theoretical expectations—suggesting unknown or misunderstood physics at play.
Looking Ahead With AI and New Telescopes
M87*, on the other hand, is also spinning rapidly, though not as fast as Sgr A*. Intriguingly, it rotates in the opposite direction to the disk of material orbiting it—possibly the result of a past collision or merger with another black hole.
“The fact that our findings challenge existing theory is, of course, very exciting,” said Janssen.
“But I view our use of AI and machine learning primarily as a foundational step. Our next goal is to enhance and expand the current models and simulations. Once the Africa Millimetre Telescope is operational and contributing data, we expect to gain even more accurate insights to test general relativity in extreme environments.”
The team’s methodology and discoveries are detailed in three research papers published in Astronomy & Astrophysics.
Read the original article on: Science Alert
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