Machine learning turns first photo of black hole into ‘skinny’ donut

(CNN) The first photo ever taken of a black hole looks a bit sharper now.

Originally released in 2019, the unprecedented historic image of the supermassive black hole at the center of the galaxy Messier 87 has captured a mostly invisible celestial object using direct imagery.

The image presented the first direct visual evidence for the existence of black holes, highlighting a central dark region encapsulated by a ring of light that appears brighter on one side. Astronomers have dubbed the object the “fuzzy orange donut.”

Now the scientists have used machine learning to give the image a cleaner upgrade that looks more like a “skinny” donut, the researchers said. The central region is darker and larger, surrounded by a bright ring as hot gas falls into the black hole in the new image.

A machine learning technique was used to enhance the Event Horizon Telescope Collaboration image (left) of the supermassive black hole at the center of the Messier 87 galaxy and produce a sharper image.

In 2017, astronomers set out to observe the invisible core of the massive galaxy Messier 87, or M87, near the Virgo galaxy cluster 55 million light-years from Earth.

The Event Horizon Telescope Collaboration, called EHT, is a global network of telescopes that has captured the first photograph of a black hole. More than 200 researchers have worked on the project for more than a decade. The project was named after the event horizon, the proposed boundary around a black hole that represents the point of no return where no light or radiation can escape.

To capture an image of the black hole, scientists combined the power of seven radio telescopes around the world using very long baseline interferometry, according to the European Southern Observatory, part of the EHT. This painting effectively created a virtual telescope the same size as Earth.

“Maximum resolution” reached

Data from the original 2017 observation was combined with a machine learning technique to capture the full resolution of what telescopes first saw. The new, more detailed image, along with a study, has been released Thursday in The Astrophysical Journal Letters.

“Thanks to our new machine learning technique, PRIMO, we were able to reach the maximum resolution of the current network,” said the study’s lead author, Lia Medeiros, postdoctoral fellow in astrophysics at the School of Natural Sciences. from the Institute for Advanced Studies in Princeton, New Jersey, in a statement.

“Since we cannot study black holes up close, the detail of 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 gravity tests.”

Medeiros and other members of the EHT have developed principal component interferometric modeling, or PRIMO. The algorithm relies on dictionary learning in which computers create rules based on large amounts of material. If a computer receives a series of images of different bananas, combined with some training, it might be able to tell whether or not an unknown image contains a banana.

Computers using PRIMO analyzed more than 30,000 high-resolution simulated images of black holes to pick out common structural details. This allowed machine learning to essentially fill in the gaps in the original image.

“PRIMO is a novel approach to the difficult task of constructing images from EHT observations,” said Tod Lauer, an astronomer at the National Science Foundation’s National Optical-Infrared Astronomy Research Laboratory, or NOIRLab. “It provides a way to compensate for missing information about the observed object, which is needed to generate the image that would have been seen using a single gigantic Earth-sized radio telescope.”

Advancing Black Hole Research

According to NASA, black holes are made up of huge amounts of matter compressed into a small area, creating a massive gravitational field that attracts everything around it, including light. These powerful celestial phenomena also have the ability to superheat the matter around them and warp space-time.

Matter accumulates around black holes, is heated to billions of degrees and reaches almost the speed of light. The light bends around the gravity of the black hole, which creates the ring of photons visible in the image. The shadow of the black hole is represented by the dark central region.

The visual confirmation of black holes also acts as confirmation of Albert Einstein’s theory of general relativity. In the theory, Einstein predicted that dense, compact regions of space would have such intense gravity that nothing could escape them. But if heated material in the form of plasma surrounds the black hole and emits light, the event horizon could be visible.

The new image may help scientists make more precise measurements of the black hole’s mass. Researchers can also apply PRIMO to other EHT observations, including those of the black hole at the center of our galaxy, the Milky Way.

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

#Machine #learning #turns #photo #black #hole #skinny #donut

Leave a Reply

Your email address will not be published. Required fields are marked *