A neural network was able to quickly extract information from images of gravitational lensing.

Gravitational lensing is when the image of a distant object in space -- like a galaxy, for example -- is distorted and multiplied by the gravity of a massive object, such as a galaxy cluster, lying in front of the smaller, faraway object. It's a useful phenomenon that has helped scientists discover exoplanets, understand galaxy evolution, spot a super bright galaxy, detect black holes and prove Einstein right. But analyzing images affected by gravitational lensing takes a really long time, requiring researchers to compare real images with simulated ones. Just one lensing effect can take weeks or months to analyze.

But researchers at Stanford and the SLAC particle accelerator have found a way to reduce that time down to just a fraction of a second. The research team trained a neural network with half a million simulated lensing images over the course of a day. Afterwards, the networks -- the team tested four different types -- were able to pull out information from the images with precision rivaling that of traditional methods.

"The amazing thing is that neural networks learn by themselves what features to look for," Phil Marshall, a researcher with the project, said in a statement. "This is comparable to the way small children learn to recognize objects. You don't tell them exactly what a dog is; you just show them pictures of dogs." Another researcher, Yashar Hezaveh, added that in this case, "It's as if they not only picked photos of dogs from a pile of photos, but also returned information about the dogs' weight, height and age."

With new telescopes being built that will surely uncover more and more examples of lensing, faster methods like this one will be needed to sift through all of the data. And importantly, the neural network analyses can be done on just a laptop or a cell phone.

The team's research was recently published in Nature and a second paper is currently being considered for publication in The Astrophysical Journal Letters. You can access a version of that article on arXiv.

Source:
courtesy of ENGADGET

by Mallory Locklear, @mallorylocklear

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