Scientists reveal enhanced image of black hole

The release of the first image of a black hole in 2019 was hailed as a significant scientific achievement. But truth be told, the photograph was a little blurry, or, as one astrophysicist involved in the research put it, a “fuzzy orange donut.”

Scientists on Thursday unveiled a new and improved image of this black hole, a giant at the center of a nearby galaxy. The research extracted the same data used to produce the previous image, but improved resolution by using image reconstruction algorithms to fill in gaps in the original observations.

“I affectionately refer to the previous image as the ‘fuzzy orange donut’ and have been referring to this new image as the ‘skinny donut’, which sounds incredibly unappetizing. We also discussed ‘diet donut’, which is equally unappetizing,” said the Brazilian astrophysicist Lia Medeiros, from the Institute for Advanced Studies in Princeton, in the State of New Jersey, main author of the research published in the Astrophysical Journal Letters.

Difficult to observe by their very nature, black holes are celestial entities that exert a gravitational pull so strong that no matter or light can escape.

The ring of light seen in the new image of the black hole is about half the width of the previous picture. There is also a larger “brightness depression” at the center – which signals the black hole’s presence – caused by light and other matter disappearing inside the object.

The picture remains somewhat blurry due to the limitations of the data supporting it – not quite ready for a Hollywood sci-fi blockbuster, but an improvement over the 2019 version.

This supermassive black hole resides in a galaxy called Messier 87, or M87, about 54 million light-years from Earth. A light year is the distance that light travels in one year, 9.5 trillion kilometers. This galaxy, with a mass of 6.5 billion times that of our Sun, is larger and more luminous than the Milky Way.

The four study authors are members of the Event Horizon Telescope (EHT) project, the international collaboration started in 2012 with the aim of directly observing the environment at the edge of a black hole. A black hole’s event horizon is the point beyond which anything – stars, planets, gas, dust and all forms of electromagnetic radiation – is sucked in by gravitational pull.

The image of the black hole M87 derives from data collected by seven radio telescopes at five locations on Earth that essentially create a planet-sized observation antenna.

“As a result, our telescope array has a lot of ‘holes’ and we need to rely on algorithms that allow us to fill in the missing data,” said Dimitrios Psaltis, co-author of the research and an astrophysicist at Georgia Tech.

The machine learning technique the team used is called PRIMO, which stands for “principal component interferometric modeling”.

“This is the first time we’ve used machine learning to fill in the holes where we don’t have data,” Medeiros said. “We used a large dataset obtained by high-fidelity simulations as the training environment and found a picture that is consistent with the data and also broadly consistent with our theoretical expectations.”

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