When astronomers showed the world their first look at a supermassive black hole in 2019, the image was likened to a flaming space doughnut.
But the team has released a new sharper image of the black hole that lurks at the center of the Messier 87 galaxy, with its black hole-ness more clearly defined.
With the help of artificial intelligence, some of the researchers worked together to leverage the full resolution of the network of radio telescopes that captured it, cleaning up the data to expose a more abundant dark centre, surrounded by a bright gas ring.
"Since we cannot study black holes up close, the detail of an image plays a critical role in our ability to understand its behaviour," said Lia Medeiros of the Institute for Advanced Study in a statement. She is the lead author of a new study on the technique published in The Astrophysical Journal Letters.
Up until four years ago, any depiction of a black hole was merely an artist's interpretation or a computer model of what the spinning, spacetime-bending phenomenon might look like. This image, however, is the real deal, each pixel representing a Herculean effort: hundreds of scientists around the globe collecting, processing, and piecing together fragments of data.
Though black holes are by definition unseeable — light can't travel fast enough to escape their clutches — the cosmic object revealed itself in silhouette: What's shown in the image is actually the hole's shadow, surrounded by the bright glow of the gas and debris swirling around its perimeter.
This supermassive black hole, dubbed M87*, is about 53 million light-years away in the Virgo constellation. But astronomers targeted it before they attempted imaging the black hole at the center of our own galaxy, Sagittarius A*, because of how humongous it is. They have estimated it's as large as our eight-planet solar system and weighs several billion times the mass of the sun.
To collect the massive amount of data needed to process the original image, the Event Horizon Telescope group used very long-baseline interferometry, which syncs up radio dishes around the world and takes advantage of Earth's rotation to form one virtual planet-sized telescope.
But since it isn't possible to cover the planet's entire surface with telescopes, gaps exist in the data like missing puzzle pieces. That's where a new technique, called PRIMO, has come in.
PRIMO, short for principal-component interferometric modelling, relies on dictionary learning, a branch of machine learning that enables computers to generate rules based on large sets of training material. Computers analyzed over 30,000 simulated images of black holes, studying how to estimate the missing pieces of the image.
The technique could be used for other Event Horizon Telescope observations, according to the team, including those of Sgr A*, the supermassive black hole in our own galaxy. Astronomers released an image of the center of the Milky Way last year.
Black holes are some of the most elusive things in outer space. The most common kind called a stellar black hole, is often thought to be the result of an enormous star dying in a supernova explosion. The star's material then collapses onto itself, condensing into a relatively tiny area.
But how supermassive black holes, millions to billions of times more massive than the sun, form is even more mysterious. Many astrophysicists and cosmologists believe these behemoths lurk at the center of virtually all galaxies. Recent Hubble Space Telescope observations have bolstered the theory that supermassive black holes get their start in the dusty cores of starburst galaxies, where new stars are rapidly churned out, but scientists are still researching the phenomenon.
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