Observations at the different sites were coordinated using atomic clocks, called hydrogen masers, accurate to within one second every million years. And, on one night in April , everything came together. The sheer volume of data generated was also unprecedented — in one night the EHT generated enough data to fill half a tonne of hard drives. This meant waiting for half a year for the South Pole data, which could only be shipped out at the end of Antarctic winter. The observations are already giving scientists new insights into the weird environment close to black holes, where gravity is so fierce that reality as we know it is distorted beyond recognition.
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At the event horizon, light is bent in a perfect loop around the black hole, meaning if you stood there you would be able to see the back of your own head. Scientists are also hoping to understand more about the origin of jets of radiation that are blasted out from the poles of some black holes at close to the speed of light, creating brilliant beacons that can be picked out across the cosmos.
The answer may be maybe not. The Event Horizon Telescope relies on a technique called interferometry.
Similarly, with the EHT, the signals from all eight telescopes have to be combined and fed through a computer to turn a mountain of incomprehensible blips into a visual picture. This presented an unprecedented computational challenge: the amount of data collected was so enormous that it had to be physically shipped to a central location, the MIT Haystack observatory, in the form of half a tonne of hard drives.
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Developing new, sophisticated algorithms was a crucial part of turning the EHT data into an image. These needed to not only combine the data but also filter out noise caused by factors like atmospheric humidity, which warps radio waves, and precisely synchronising the signals captured by the far-flung telescopes.
While still studying at MIT, the computer scientist Katie Bouman came up with a new algorithm to stitch together data collected across the EHT network. At one stage, this involved the collaboration splitting into four separate teams which analysed the data independently until they were absolutely confident of their findings.
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