How to See a Black Hole Without Trusting Telescopes ✛
Imagine a jigsaw puzzle with the box lid missing, but every piece is guaranteed to be authentic. That's roughly how astronomers reconstruct images of black holes: signals from a network of radio telescopes are combined into special combinations that are immune to errors from individual stations. These combinations are called closure invariants: they're like the sum of a triangle's angles, which is always 180 degrees no matter how crooked your protractor is.
Testing on real data confirmed: for the blazar 3C279, the algorithm spotted three bright knots, one of which appeared to move at ten times the speed of light (an illusion caused by a jet heading almost straight toward us). For Centaurus A, it produced two glowing edges along the jet, and their opening angle matched the Event Horizon Telescope's results. Previously, only one collaboration had produced such images, raising doubts: was the processing injecting what people wanted to see? Now there's an independent witness, trained on earthly pictures, that still discerned cosmic jets.
Closure invariants were used as early as the 1960s to discover the double structure of the radio galaxy Cygnus A without precise antenna calibration. In this work, the algorithm is trained on photographs of cats and airplanes from the CIFAR-10 dataset, yet it still recovers astrophysical jets.
In Carl Sagan's novel 'Contact', the protagonist uses interferometry to detect an extraterrestrial signal; here a similar principle is applied to imaging black holes. In the movie 'Interstellar', the image of Gargantua was created using simulations, not real observations — GenDIReCT brings us closer to obtaining such pictures from data.