Astronomers capture first-ever image of a black hole

Astronomers capture first-ever image of a black hole

An international team of radio astronomers announced today (April 10) the first close-up image of a black hole.

In April 2017, scientists used a global network of telescopes to see and capture the first-ever picture of a black hole, according to an announcement by researchers at the National Science Foundation Wednesday morning. They captured an image of the supermassive black hole and its shadow at the center of a galaxy known as M87.

This is the first direct visual evidence that black holes exist, the researchers said. In the image, a central dark region is encapsulated by a ring of light that looks brighter on one side.

The massive galaxy, called Messier 87 or M87, is near the Virgo galaxy cluster 55 million light-years from Earth. The supermassive black hole has a mass that is 6.5 billion times that of our sun.


“We have seen what we thought was unseeable,” said Sheperd Doeleman, director of the Event Horizon Telescope Collaboration. “We have seen and taken a picture of a black hole.”
The Event Horizon Telescope Collaboration, called EHT, is a global network of telescopes that captured the first-ever photograph of a black hole. More than 200 researchers were involved in the project. They have worked for more than a decade to capture this. The project is named for the event horizon, the proposed boundary around a black hole that represents the point of no return where no light or radiation can escape.
In their attempt to capture an image of a black hole, scientists combined the power of eight radio telescopes around the world using Very-Long-Baseline-Interferometry, according to the European Southern Observatory, which is part of the EHT. This effectively creates a virtual telescope around the same size as the Earth itself.
The telescopes involved in creating the global array included ALMA, APEX, the IRAM 30-meter telescope, the James Clerk Maxwell Telescope, the Large Millimeter Telescope Alfonso Serrano, the Submillimeter Array, the Submillimeter Telescope and the South Pole Telescope
The telescope array collected 5,000 trillion bytes of data over two weeks, which was processed through supercomputers so that the scientists could retrieve the images.
Details of the observation were published in a series of six research papers published in The Astrophysical Journal Letters.

How 29-Year-Old Computer Scientist Katherine Bouman Helped Bring Us The First Image of a Black Hole


Katherine Bouman developed  An algorithm that had stitched together a picture of a black hole. Bouman, 29, a postdoctoral researcher at the Harvard-Smithsonian Center for Astrophysics, had been working on such an algorithm for almost six years since she was a graduate student at MIT.

She was one of about three dozen computer scientists who used algorithms to process data gathered by the Event Horizon Telescope project, a worldwide collaboration of astronomers, engineers, and mathematicians.

Telescopes around the world collected high-frequency radio waves from the vicinity of Messier 87 (M87), a galaxy with a supermassive black hole 54 million light-years away.

But atmospheric disturbance and the spareness of the measurements meant “an infinite number of possible images” could explain the data, Bouman said. Well-designed algorithms had to crunch through the chaos.

“We blurred two of the images and then averaged them to the other one to get the image that we showed today,” Bouman said. The ring of material that surrounds M87*, which has a mass of 6.5 billion suns, “is something that we were incredibly confident about.”


How to take a picture of a black hole | Katie Bouman

The Washington Post spoke with Bouman after the picture was unveiled. The following is lightly edited for clarity.

We have telescopes distributed around the world. For every two telescopes in the telescope array, we measure a single spatial frequency, which tells you something about like how fast things are changing.

We get this partial information. It’s almost like seeing one pixel in an image (but it’s in a different kind of domain). We have to come up with methods that take this really sparse, really noisy data, and try to find the image that might have caused those measurements.

What we have to end up doing is imposing things called “regularizers” or “priors” that allow us to say, “Okay, of all of the images that possibly could fit this data, this set of images is most likely.”

But the danger is we don’t want to inject additional information into the problem, so as to bias our result toward something that we would expect to see. We have spent an enormous amount of time making sure that what we were seeing was actually real and not just something that, even subconsciously, we might have imposed on the data.

[To remove the possibility of bias shared by the entire team, the project split its computer-imaging experts into four different groups, each working on a different sort of algorithm. They were not allowed to communicate.]



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