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Bouman, a 29-year-old postdoctoral fellow at the Harvard-Smithsonian Astrophysical Center, has been working on such an algorithm for nearly six years as a graduate student at MIT. She was one of three dozen computer scientists using algorithms to process the data collected by the Event Horizon Telescope project, a global collaboration of astronomers, engineers and mathematicians.
Video: Katie Bouman was a postdoctoral student at MIT when she led a team that designed one of the algorithms to analyze the data at the origin of the first images of the year. a black hole. (Adriana Usero / The Washington Post)
Telescopes around the world were capturing high-frequency radio waves near Messier 87, a supermassive black hole located 54 million light-years away. But atmospheric disturbance and measurement accuracy meant "that an infinite number of possible images" could explain the data, Bouman said. Well-designed algorithms have had to go through chaos.
The image shared Wednesday, which has been compared to a donut melt, the Eye of Sauron or even a Rembrandt, is a composite of several of these reconstructions. "We scrambled two of the images and then averaged them to get the image we showed today," Bouman said. The ring of material that surrounds Messier 87, which has a mass of 6.5 billion suns, "is something for which we had incredible confidence."
The Washington Post spoke with Bouman after the unveiling of the photo. The following is slightly edited for clarity.
Q: You are not an astronomer. How did you participate in the shooting of a black hole?
A: I come from a background in computer science and electrical engineering. I did my PhD in a computer vision group, where you try to understand the images. And I heard about this project, from this idea of black hole imagery. At the time, I did not even know what a black hole is. But I accompanied this meeting [where Shep Doeleman, the Harvard University astronomer who directs the Event Horizon Telescope project, was discussing black holes]. I did not know what he was talking about, but when I left the meeting, I knew it was something I wanted to work on.
I'm interested in how we can see or measure things that we think are invisible. And how can we find unique ways to merge instrumentation and algorithms to measure things that you can not measure with standard instruments.
Q: What was the role of the algorithm for this image, collecting telescope data from the planet?
A: We have telescopes distributed around the world. For each group of two telescope telescopes, we measure a single spatial frequency, which tells you something like the speed at which things change.
We get this partial information. It's almost like seeing a pixel in an image (but it's in a different kind of domain). We need to find methods that take these very rare and very noisy data and try to find the image that might have caused these measurements.
What we must end up doing is imposing things called "regularizers" or "priors" that allow us to say, "OK, of all the images that may possibly contain these data, this set is most likely."
But the danger is that we do not want to inject additional information into the problem, in order to skew our results towards something we expected to see. We spent a lot of time making sure what we were seeing was real and not just something that, even unconsciously, we could have imposed on the data.
(To eliminate the possibility of bias shared by the entire team, the project divided its computer imaging experts into four different groups, each working on a different type of algorithm.They were not allowed to communicate.)
Q: When did you know that the black hole was, well, a hole?
A: We were at least convinced to see this characteristic in the shape of a ring. However, we did not know that the other teams would get the same result.
We all met at a meeting in Cambridge, Massachusetts, and on the second day of the meeting, we all revealed the image we had reconstructed from the data. It was probably the most exciting time I ever had with the project.
When I saw that we had all rebuilt this ring, I knew that it was an incredibly robust feature.
(For months, computer scientists have tried to break their images, they have developed new scripts or pipelines and formed these pipelines to disk data, these astronomical structures have no flaws, the pipelines developed for the disks they have rebuilt a ring.
We did not have a record. We always have this hole.
This article was written by Ben Guarino, a Washington Post reporter.
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