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Machine learning used to sharpen the first image of a black hole

Georgina Torbet
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The world watched in delight when scientists revealed the first-ever image of a black hole in 2019, showing the huge black hole at the center of galaxy Messier 87. Now, that image has been refined and sharpened using machine learning techniques. The approach, called PRIMO or principal-component interferometric modeling, was developed by some of the same researchers that worked on the original Event Horizon Telescope project that took the photo of the black hole.

That image combined data from seven radio telescopes around the globe which worked together to form a virtual Earth-sized array. While that approach was amazingly effective at seeing such a distant object located 55 million light-years away, it did mean that there were some gaps in the original data. The new machine learning approach has been used to fill in those gaps, which allows for a more sharp and more precise final image.

A team of researchers, including an astronomer with NSF’s NOIRLab, has developed a new machine-learning technique to enhance the fidelity and sharpness of radio interferometry images. To demonstrate the power of their new approach, which is called PRIMO, the team created a new, high-fidelity version of the iconic Event Horizon Telescope's image of the supermassive black hole at the center of Messier 87, a giant elliptical galaxy located 55 million light-years from Earth. The image of the M87 supermassive black hole originally published by the EHT collaboration in 2019 (left); and a new image generated by the PRIMO algorithm using the same data set (right).
The image of the M87 supermassive black hole originally published by the Event Horizon Telescope collaboration in 2019 (left); and a new image generated by the PRIMO algorithm using the same data set (right). L. Medeiros (Institute for Advanced Study), D. Psaltis (Georgia Tech), T. Lauer (NSF’s NOIRLab), and F. Ozel (Georgia Tech)

“With our new machine-learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” said lead author of the research, Lia Medeiros of the Institute for Advanced Study, in a statement. “Since we cannot study black holes up close, the detail in an image plays a critical role in our ability to understand its behavior. The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity.”

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PRIMO was trained using tens of thousands of example images which were created from simulations of gas accreting onto a black hole. By analyzing the pictures that resulted from these simulations for patterns, PRIMO was able to refine the data for the EHT image. The plan is that the same technique can be used for future observations from the EHT collaboration as well.

“PRIMO is a new approach to the difficult task of constructing images from EHT observations,” said another of the researchers, Tod Lauer of NSF’s NOIRLab. “It provides a way to compensate for the missing information about the object being observed, which is required to generate the image that would have been seen using a single gigantic radio telescope the size of the Earth.”

In 2022, the EHT collaboration followed up its image of the black hole in M87 with a stunning image of the black hole at the heart of the Milky Way, so that image could be the next target for sharpening using this technique.

“The 2019 image was just the beginning,” said Medeiros. “If a picture is worth a thousand words, the data underlying that image have many more stories to tell. PRIMO will continue to be a critical tool in extracting such insights.”

The research is published in The Astrophysical Journal Letters.

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Georgina Torbet
Georgina Torbet
Space Writer
Georgina is the Digital Trends space writer, covering human space exploration, planetary science, and cosmology. She…
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