See an even sharper image of a supermassive black hole

The first image of a black hole released by astronomers in 2019 was amazing, surprising, awe-inspiring and all that jazz, but it was (to be perfectly clear) faint. Even to concerned astronomers, it appeared to be a “blurry, orange doughnut” seen through thick cosmic fog.

But then more science happened, and now the world has a sharper image of a black holeā€”in this case, a supermassive one sitting in the giant galaxy Messier 87, 54 million light-years away.

The sharp image will help theorists better understand the physics of black holes, while the technology used to create it could be used for other types of research, including the study of alien planets orbiting distant stars.

The A newly processed imagePublished Thursday in the Astrophysical Journal Letters, it used machine learning to fill in a lot of data that was missing from the original. Acquired by the Event Horizon Telescope. The EHT is not a single instrument, but rather a constellation of telescopes across the planet that collected data in a technique called very long baseline interferometry.

This method is so powerful that it allows astronomers to effectively mirror a telescope through an Earth-sized dish. But that telescope is riddled with holes, and missing observational data poses a challenge for astronomers.

Enter “PRIMO”, a machine learning algorithm that has been specifically trained on thousands of people. High-fidelity simulations of matter falling into black holes.

The new image captures radiation emitted by heated matter as it falls into a black hole. That light ring is about 2.6 times the diameter of the “event horizon”. Author of the book Leah MedeirosAn astrophysicist at the Institute for Advanced Study in Princeton, NJ

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“The event horizon is not an observable feature. What we see is what we call the black hole shadow,” Medeiros said.

The next target for enhanced images is Sagittarius A* (also known as Sgr A*, pronounced “chadge-a-star”), the supermassive black hole at the center of our own galaxy whose EHT image was last published. year.

Harvard astrophysicist and head of the EHT collaboration, Shepard Douleman, praised the creativity of the new approach, although he noted that machine learning has some limitations.

PRIMO “relies partly on the truth of these computer models to fill in the gaps in EHT,” he said. “This helps produce slightly crisper images, but whether the images are more accurate will require validation when we have more data.”

And EHT is still a long way off. Tolman said his team is now doubling the number of telescopes around the planet, operating at a wide range of radio frequencies, that monitor black holes. These include small radio telescopes at optimal locations around the world. That effort will fill many imaging gaps and help produce high-resolution images, including movies.

“With more data, we’ll all soon see real black hole ‘cinemas,'” he said.

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