Artificial intelligence could help us see farther into space than ever before前所未有！人工智能让我们更清楚地观看太空 时间:2017-09-09 单词数:4220
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Distortions in space-time sound like they’d be more of a concern on an episode of Star Trek than they would in the real world. However, that’s not necessarily true: analyzing images of gravitational waves could help enormously extend both the range and resolution of telescopes like Hubble, and allow us to see farther into the universe than has been possible before.
The good news? Applying an artificial intelligence neural network to this problem turns out to accelerate its solution well beyond previous methods -- like 10 million times faster. That means that analysis which could take human experts weeks or even months to complete can now be carried out by neural nets in a fraction of a single second.
Developed by researchers at Stanford University and the SLAC National Accelerator Laboratory, the new neural network is able to analyze images of so-called gravitational lensing. This is an effect first hypothesized about by Albert Einstein, who suggested that giant masses such as stars have the effect of curving light around them. This effect is similar to a telescope in that it allows us to examine distant objects with more clarity. However, unlike a telescope, gravitational lenses distort objects into smeared rings and arcs -- so making sense of them requires the calculating abilities of a computer.
To train their network, researchers on the project showed it around half a million simulated images of gravitational lenses. After this was done, the neural net was able to spot new lenses and determine their properties -- down to how their mass was distributed, and how great the magnification levels of the background galaxy were.
Given that projects like the Large Synoptic Survey Telescope (LSST), a 3.2-gigapixel camera currently under construction at SLAC, is expected to increase the number of known strong gravitational lenses from a few hundred to tens of thousands, this work comes at the perfect time.
"We won’t have enough people to analyze all these data in a timely manner with the traditional methods," said postdoctoral fellow Laurence Perreault Levasseur, a co-author on the associated Nature research paper. "Neural networks will help us identify interesting objects and analyze them quickly. This will give us more time to ask the right questions about the universe."
《自然》的论文合著者、博士后研究员Laurence Perreault Levasseur说：“我们没有足够的人用传统方法及时分析这些数据，而神经网络将帮助我们识别有趣的对象并快速分析它们。这将给我们更多时间来考虑关于宇宙的恰当问题。”
Impressively, the neural network doesn’t even need a supercomputer to run on: one of the tested neural nets was designed to work on an iPhone. Studying the universe in greater detail than ever? Turns out there’s an app for that!
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