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 sheer volume of data generated was also unprecedented – in one night the EHT generated enough data to fill half a tonne of hard drives. “We got super lucky, the weather was perfect,” said Ziri Younsi, a member of the EHT collaboration who is based at University College London. And, on one night in April 2017, everything came together. Observations at the different sites were coordinated using atomic clocks, called hydrogen masers, accurate to within one second every 100 million years. The success of the project hinged on clear skies on several continents simultaneously and exquisite coordination between the eight far-flung teams. “We hope to get that very soon,” said Doeleman. The collaboration is still working on producing an image of the Milky Way’s black hole. The second target, which yielded the image, was a supermassive black hole in the galaxy M87, into which the equivalent of 6bn suns of light and matter has disappeared. First was Sagittarius A*, the black hole at the centre of the Milky Way, which has a mass of about 4m suns. When observations were launched in 2017, the EHT had two primary targets. The EHT achieved the necessary firepower by combining data from eight of the world’s leading radio observatories, including the Atacama Large Millimetre Array (Alma) in Chile and the South Pole Telescope, creating an effective telescope the size of the Earth. This was once thought to be an insurmountable challenge. A lot of this material is destined for oblivion, although some of it is ejected as powerful jets of radiation.īut black holes are so small, dark and distant that observing them directly requires a telescope with a resolution equivalent to being able to see a bagel on the moon. This is the point at which escaping would require something to travel at faster than the speed of light – which as far as we know nothing does – so it is the point of no return.īlack holes are surrounded by an accretion disk of dust and gas, orbiting at close to the speed of light. The edge of the black hole is defined by its so-called event horizon. The equations predicted that, beyond a certain threshold, when too much matter or energy is concentrated in one place, space and time collapse, leaving behind a sinkhole through which light and matter can enter but not escape.Īt first these were thought to be mathematical oddities, rather than real astronomical objects, but in the past century overwhelming evidence has confirmed that black holes are out there. Medeiros said that the way in which PRIMO filled in that missing data was "a way that has never been done before by using machine learning.Black holes were first predicted by Einstein’s theory of general relativity, which reimagined gravity as the warping of space and time by matter and energy. "it provides a way to compensate for the missing information about the object being observed, which is required to generate the image that would have been seen using a single gigantic radio telescope the size of Earth," Lauer said. Doing that allowed the system to essentially fill in the blanks of what was missing in the 2019 image. ![]() In this case, they had computers look at more than 30,000 pictures of black holes taking in gas. Medeiros and others developed the PRIMO modeling system, which co-developer Tod Lauer says is a "new approach to a difficult task." That system used a type of machine learning that lets computers make rules based on large sets of "training material," AIS said. The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity." ![]() "Since we cannot study black holes up-close, the detail of an image plays a critical role in our ability to understand its behavior. ![]() "With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array," lead author of the research Lia Medeiros said in a press release.
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