Dr. katie bouman

Imaging scientist katie Bouman aided construct the first ever photo of a schwarze farbe hole, yet she didn’t expect this kind of excited — or attention.

Du schaust: Dr. katie bouman

The occasion Horizon Telescope released the bild on Wednesday, und since climate Bouman has actually been swamped with phone calls, text messages and emails. Back she was not the only woman kommen sie work top top this bild (more 보다 200 scientists around the world added to die project), she has end up being a symbol zum women’s achievement in computer science und astronomy. Political numbers like Alexandria Ocasio-Cortez have encouraged Bouman kommen sie “take your rightful seat bei history.”

But what specifically did the 29-year-old Bouman do zu capture in image von the supermassive schwarz hole at die center of the M87 galaxy, situated 55 million irradiate years away?

“No telescope actually takes a picture,” Bouman told die biologischelandbouw.org NewsHour in bei interview. Instead, all von the disparate charme collected über the planet-sized telescope back an 2017 needed kommen sie be processed and translated into bei image.

On Wednesday after ~ the bild was released, Bouman explained to NewsHour exactly how she crafted in algorithm kommen sie incorporate that all.

The conversation has been edited for length and clarity.

How did freundin get involved bei the occasion Horizon Telescope project?


I hardly knew what a schwarz hole was, but i remember thinking punkt the ende of ns meeting that ich really wanted zu work top top this project.

I did my PhD in computer vision at ns Computer Science and Artificial intelligence Laboratory at MIT, working on evaluating images and understanding images. I just love images. , i heard around this meeting, und decided to arbeit along kommen sie hear Shep Doeleman and a couple of other people with die Event Horizon Telescope group.

I sat in on the meeting zum like, 2 hours, and I understood almost nothing Shep said. Ich hardly knew what a schwarz hole was, but ich remember thinking weist the end of die meeting that ich really wanted kommen sie work on this project.

They to be interested an getting someone to start working ~ above imaging, because at ns time lock were blieb trying kommen sie get the instrument together. They hadn’t really obtained into what they to be going to do when they got the data.

Wait, rewind. What do you mean, obtaining the werkzeug together?

In order to seen a black hole, sie need in Earth-sized telescope.

That schwarze farbe hole is so tiny indigenous Earth, that about the same as if you were trying zu see an orange on ns surface of the moon.


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This GIF compares ns pixel size von the many technologically progressed camera on die Hubble space Telescope v the image of die supermassive schwarze farbe hole bei the M87 galaxy. Image courtesy of Alex Parker


The law of diffraction says that if freundin know die resolution sie need zu achieve, and the wavelength freundin are observing at, then you can figure out what your telescope size need to be.

We needed bei Earth-sized telescope, and obviously we couldn’t build bei Earth-sized telescope dish. Instead, we took eight various telescopes from all around the world that were built for other purposes, und we join them together zu act as one dish.

That’s what die Event Horizon Telescope is.

Once this telescope was put together, what did you do?

We had actually telescopes und observers bei Hawaii und Chile und Mexico und Spain, and they all had to schutz good weather at die same time, down to die picosecond . I observed native Mexico, 15,000 feet above sea level.

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No telescope in reality takes a picture. What happens is, the light native the schwarze farbe hole travels 55 million light years und then every food collects a einzel stream des the light that it watch at die same time.

That’s videotaped onto these tough drives. We can’t send that säule over the internet because it’s way too big — sent on airplanes kommen sie a main location, where they’re computationally processed.


Imaging scientist katie Bouman watching the first reconstructed bild of a schwarz hole emerge from ns Event Horizon Telescope data. Bild courtesy of katie Bouman


But the incomplete.

The process von imaging ist taking the incomplete die info that we obtain from a couple of places top top our digital telescope, and trying to fill in all die missing die info to get die picture in actual Earth-sized telescope would schutz produced.

That is a hard problem.

How do freundin combine the information und get in image you trust?

There’s in infinite number von possible images that could have been produced from die sparse measurements that we took. Die goal von imaging zu sein to uncover the bild that notfall only reconstructs und matches the data that us measured, but so is ns one that ist most likely.

We schutz to i charged some die info about what the bild should look at like an order kommen sie recover that image. Some stuff that us impose ist natural and easy — we recognize that light zu sein positive. You can’t schutz negative light.

Other dinge we might impose would certainly be how smooth the image is. Sie wouldn’t expect bei image des a black hole kommen sie look like ns white noise sie get wie man you pull a cable out of your television.


Scientists have obtained the first image of a schwarze farbe hole, using event Horizon Telescope observations von the center of the galaxy M87. The bild shows a bright runde formed as light bends bei the extreme gravity around a black hole that zu sein 6.5 billion mal more enormous than die Sun. This long-sought bild provides ns strongest evidence kommen sie date zum the existence des supermassive schwarz holes und opens a new window onto die study of schwarz holes, their event horizons, and gravity. Photo von Event Horizon Telescope Collaboration


You really don’t want to accidentally tell ours imaging algorithms that, weil das example, “Oh, what zu sein likely ist this runde shape,” due to the fact that then we just recover that runden back, and we’ve learned nothing.

To avoid shared bias, we break-up ourselves right into four various teams that had various focuses und different kinds des algorithms. We functioned separately zum a month, not talking zu each other around anything.

Then after ~ one month we all gathered together bei Cambridge, Massachusetts, and we put all the bildern up top top a screen hinweisen one time. I think that was die most exceptional moment, due to the fact that even despite each of the other bild had various underlying assumptions and looked different, this runde appeared bei all of the images.


The runden was always the same size, und it was brighter in the south. That was huge.

The ring was always the same size, und it was brighter bei the south. That was huge.

That was bei late July of belastung year. Because then, we’ve invested months trying kommen sie break our images über training our algorithms on man-made data.

Even wie man we then applied those algorithms to the real data, we still got die ring in the end. Sie would oase to bend over stunner backwards to not get this ring.

In ns end, what was shown today was from three different pipelines, three various methods that we trained on die synthetic data. We got in image native each des those, and we blurred und averaged castle together deshalb they were all consistent.

Now what?


…the project brought in people from so many various areas. That’s what made it possible, no one person did this.

ich haven’t referred to as my family members yet, ich haven’t zeigen them ns image. I’ve been lips-sealed weil das a year, and so I in excited zu get the chance to talk kommen sie them. I didn’t expect…I’ve been obtaining messages like crazy all day. I’m excited that world are so excited.

But you know, this was a team effort. I don’t know why i’m getting deshalb much press myself…lots of people, handling those petabytes of data, it is what made it possible.

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So many human being from ns imaging mannschaft really must be recognized — Andrew Chael, Kazunori Akiyama, michael Johnson und Jose Gomez.

I brought ns computer science mindset, but die project brought in people from deshalb many various areas.

That’s what made the possible, no one personen did this.


Left: katie Bouman, imaging scientist, posed near tough drives containing some des the petabytes of schwarze farbe hole imaging data collected über telescopes around the world. Bild courtesy of katie Bouman


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