Astronomers Use Machine Learning to Enhance First Black Hole Image: The PRIMO Algorithm

2023-04-18 11:31:38

a supermassive black hole M87 originally imaged by the EHT collaboration in 2019 (left); and a new image generated by the PRIMO algorithm using the same dataset (right). Credit: Medeiros et al. 2023

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A black hole is a place in space where the gravitational field is so strong that not even light can escape it. Astronomers classify black holes into three categories by size: miniature, stellar, and supermassive black holes. Miniature black holes might have a mass smaller than our Sun and supermassive black holes might have a mass equivalent to billions of our Sun.

” 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 regarding the behavior of black holes, and regarding 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, regarding 55 million light-years from Earth, dazzled the scientific world in 2019. The image was produced by combining observations from a global network of radio telescopes — but data gaps mean that the image It was incomplete and somewhat blurry.

In a study published last week in the Letters from The Astrophysical JournalAn international team of astronomers described how they filled in the gaps by analyzing more than 30,000 images of simulated black holes.

“Thanks to a new machine learning technique, PRIMO, we were able to reach the maximum accuracy of the existing network,” said the study’s lead author, Leah Medeiros of the Institute for Advanced Study, in a press release.

PRIMO refinement and refinement of the EHT view of the ring of hot matter orbiting the black hole as it falls into a gravitational singularity. Medeiros explained that it does more than just picture prettier.

“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 pictured now is regarding a factor smaller, which will be a strong limitation for our theoretical models and gravity tests.”

The technique developed by Medeiros and his colleagues – known as Principal component interferometry modeling, or PRIMO for short, analyzes large datasets of training images to determine the most probable ways to fill in the missing data. It is similar to how researchers used artificial intelligence to analyze the musical works of Ludwig von Beethoven Produces a score for the composer’s Unfinished Tenth Symphony.

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Tens of thousands of simulated EHT images have been fed into the PRIMO model, covering a wide range of structural models of the swirling gas of the M87 black hole. Simulations that best match the available data were combined to produce high-fidelity reconstructions 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 determinations, in turn, might 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 refine the Event Horizon Telescope’s hazy view of Sagittarius A*, the supermassive black hole at our center.[{”attribute=””>MilkyWaygalaxyAndthat’snotall:ThemachinelearningtechniquesemployedbyPRIMOmightbeappliedtomuchmorethanblackholes“Thismighthaveimportantimplicationsforinterferometrywhichplaysaroleinfieldsfromexoplanetstomedicine”Medeirossaid[{”attribute=””>MilkyWaygalaxyAndthat’snotall:ThemachinelearningtechniquesemployedbyPRIMOmightbeappliedtomuchmorethanblackholes“Thismighthaveimportantimplicationsforinterferometrywhichplaysaroleinfieldsfromexoplanetstomedicine”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