Astronomers use machine learning to improve black hole image: PRIMO algorithm aids in understanding black hole behavior and testing gravitational theories.

” data-gt-translate-attributes=”[{“attribute=””>blackhole[{“attribute=””>blackhole image, aiding in black hole behavior understanding and testing gravitational theories. The new technique, called PRIMO, has potential applications in various fields, including exoplanets and medicine.

Astronomers have used machine learning to sharpen up the Event Horizon Telescope’s first picture of a black hole — an exercise that demonstrates the value of artificial intelligence for fine-tuning cosmic observations.

The image should guide scientists as they test their hypotheses about the behavior of black holes, and about the gravitational rules of the road under extreme conditions.

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Overview of the simulations generated for the PRIMO algorithm training set. Credit: Medeiros et al. 2023

An EHT image of the supermassive black hole at the center of an elliptical galaxy known as M87, about 55 million light-years from Earth, dazzled the science world in 2019. The image was produced by combining observations from a global array of radio telescopes — but gaps in The data means that the picture was somewhat patchy and fuzzy.

In a study published last week in the Astrophysical Journal LettersAn international team of astronomers described how they filled in the gaps by analyzing more than 30,000 simulated images of a black hole.

“Using a new machine learning method, PRIMO, we were able to achieve maximum accuracy for the existing matrix,” study lead author Leah Medeiros of the Institute for Advanced Study said in a press release.

PRIMO has narrowed and sharpened the EHT’s view of the ring of hot matter orbiting the black hole as it falls into a gravitational singularity. Medeiros explained that this makes him more than just a prettier photo.

“Since we cannot study black holes closely, the details of the image play an important role in our ability to understand their behavior,” she said. “The width of the ring in the image is now about two times smaller, which will be a strong limitation for our theoretical models and gravity tests.”

The technique developed by Medeiros and her colleagues – known as Principal component interferometry modelingPRIMO, or PRIMO for short – analyzes large datasets of training images to figure out the best ways to fill in the missing data. It’s similar to the way AI researchers used to analyze Ludwig von Beethoven’s musical works Produces a score for the composer’s Unfinished Tenth Symphony.

Tens of thousands of simulated EHT images have been fed into the PRIMO model, covering a wide range of structural patterns of swirling gas in M87’s black hole. Simulations that provided the best fit to the available data were combined together to produce a high-fidelity reconstruction of the missing data. The resulting image was then reprocessed to match the actual maximum resolution of the EHT.

The researchers say the new image should lead to more accurate determinations of the mass of M87’s black hole and the extent of its event horizon and accretion ring. These decisions, in turn, could lead to more robust tests of alternative theories regarding black holes and gravity.

The clearer picture of the M87 is just the beginning. PRIMO can also be used to sharpen the Event Horizon Telescope’s fuzzy view of Sagittarius A*, the supermassive black hole at our center.[{”attribute=””>MilkyWaygalaxyAndthat’snotall:ThemachinelearningtechniquesemployedbyPRIMOcouldbeappliedtomuchmorethanblackholes“Thiscouldhaveimportantimplicationsforinterferometrywhichplaysaroleinfieldsfromexoplanetstomedicine”Medeirossaid[{”attribute=””>MilkyWaygalaxyAndthat’snotall:ThemachinelearningtechniquesemployedbyPRIMOcouldbeappliedtomuchmorethanblackholes“Thiscouldhaveimportantimplicationsforinterferometrywhichplaysaroleinfieldsfromexoplanetstomedicine”Medeirossaid

Adapted from an article originally published on Universe Today.

Reference: “The Image of the M87 Black Hole Reconstructed with PRIMO” by Lia Medeiros, Dimitrios Psaltis, Tod R. Lauer and Feryal Özel3, 13 April 2023, The DOI: 10.3847/2041-8213/acc32d