The Woman Behind The First Ever Black Hole Photo

Katie Bouman is a computer scientist who developed the algorithm that allowed scientists to take the first ever photograph of a black hole.

Bouman is a 29-year-old PhD candidate in computer science and artificial intelligence from the Massachusetts Institute of Technology (MIT) and researches emerging methods for imaging technology.

She led the development of the black hole telescope algorithm under the Event Horizon Telescope project. The project coordinated measurements from six radio telescopes from observatories across the globe to overcome the issue that no single telescope antenna dish in the world is large enough to capture the radio waves from a black hole.

Bouman is an assistant professor in computing and mathematical sciences at the California Institute of Technology. Originally from Indiana,  her father is a professor of electrical and computer engineering at Purdue University.

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According to Bouman, capturing an image of a black hole would require an antenna dish with a diameter of 10,000 kilometres, "which is not practical because the diameter of the Earth is 13,000 kilometres," she told MIT News.

Instead, she developed an algorithm called Continuous High-Resolution Image Reconstruction using Patch priors (CHIRP), which uses a process called interferometry that combines the signals detected by telescopes to interfere with one another.

Usually, radio telescopes will receive information from outer space at differing times due to lags caused by the Earth's atmosphere but Bouman developed a solution using an equation that uses the information from three telescopes at a time to cancel out delays caused by atmospheric noise.

While only six observatories are currently signed up to the project, more are expected to join in months to come, according to MIT.

The computational telescope that Bouman and her colleagues managed to devise is theoretically as large as the Earth.

Katie Bouman, MIT computer scientist. Source: MIT.

Bouman's CHIRP system also produced a more reliable image than traditional methods of astrophotography by creating a model that adjusts the information provided by the radio telescopes and using available data to meet expectations about what the black hole should look like.

Bouman also created a machine-learning algorithm to refine the black hole image and has made her algorithm data publicly available online for use.