ARTIFICIAL INTELLIGENCE COULD HELP US SEE FARTHER INTO SPACE THAN EVER BEFORE

aiuniverse — Wed, 06 Sep 2017 09:18:34 +0000

Source – digitaltrends.com

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.”

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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Artificial intelligence helps fast analyze gravitational lenses

aiuniverse — Mon, 04 Sep 2017 11:37:17 +0000

Source – news.xinhuanet.com

Researchers from the U.S. Department of Energy’s SLAC National Accelerator Laboratory and Stanford University have shown that neural networks, a form of artificial intelligence, can analyze the complex distortions in spacetime known as gravitational lenses 10 million times faster than traditional methods.

The work, by a research team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, was detailed in a study published in Nature.

The researchers used neural networks to analyze images of strong gravitational lensing, where the image of a faraway galaxy is multiplied and distorted into rings and arcs by the gravity of a massive object, such as a galaxy cluster. The distortions provide clues about how mass is distributed in space and how that distribution changes over time, which are linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that is accelerating the expansion of the universe.

Until now, analyzing such images has been a tedious process that involves comparing actual images of lenses with a large number of computer simulations of mathematical lensing models, according to a news release from SLAC, originally named Stanford Linear Accelerator Center. It can take weeks to months for a single lens.

To train the neural networks in what to look for, the researchers showed them about half a million simulated images of gravitational lenses for about a day. Once trained, the networks were able to analyze new lenses almost instantaneously with a precision that was comparable to traditional analysis methods.

Inspired by the architecture of the human brain, in which a dense network of neurons quickly processes and analyzes information, the neural networks are able to sift through large amounts of data and perform complex analyses very quickly and in a fully automated fashion, which is needed for future sky surveys that will look deeper into the universe and produce more data.

“We won’t have enough people to analyze all these data in a timely manner with the traditional methods,” postdoctoral fellow Laurence Perreault Levasseur, a co-author of the study, was quoted as saying. “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.”

In their the team’s brain-mimicking neural networks, “neurons” are single computational units that are associated with the pixels of the image being analyzed. They are organized into layers, up to hundreds of layers deep. Each layer searches for features in the image. Once the first layer has found a certain feature, it transmits the information to the next layer, which then searches for another feature within that feature.

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ARTIFICIAL INTELLIGENCE COULD HELP US SEE FARTHER INTO SPACE THAN EVER BEFORE

Artificial intelligence helps fast analyze gravitational lenses