SLAC
and Stanford researchers demonstrate that brain-mimicking ‘neural
networks’ can revolutionize the way astrophysicists analyze their most
complex data, including extreme distortions in spacetime that are
crucial for our understanding of the universe.
August 30, 2017
Menlo Park, Calif. — Researchers
from the Department of Energy’s SLAC National Accelerator Laboratory
and Stanford University have for the first time shown that neural
networks – a form of artificial intelligence – can accurately analyze
the complex distortions in spacetime known as gravitational lenses 10
million times faster than traditional methods.
“Analyses that typically take weeks to months to complete, that require the input of experts and that are computationally demanding, can be done by neural nets within a fraction of a second, in a fully automated way and, in principle, on a cell phone’s computer chip,” said postdoctoral fellow Laurence Perreault Levasseur, a co-author of a study published today in Nature.
The team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, 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, that’s closer to us. The distortions provide important clues about how mass is distributed in space and how that distribution changes over time – properties linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that’s accelerating the expansion of the universe.
Until now this type of analysis has been a tedious process that involves comparing actual images of lenses with a large number of computer simulations of mathematical lensing models. This can take weeks to months for a single lens.
But with the neural networks, the researchers were able to do the same analysis in a few seconds, which they demonstrated using real images from NASA’s Hubble Space Telescope and simulated ones.
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. In a separate paper, submitted to The Astrophysical Journal Letters, the team reports how these networks can also determine the uncertainties of their analyses.
“The neural networks we tested – three publicly available neural nets and one that we developed ourselves – were able to determine the properties of each lens, including how its mass was distributed and how much it magnified the image of the background galaxy,” said the study’s lead author Yashar Hezaveh, a NASA Hubble postdoctoral fellow at KIPAC.
This goes far beyond recent applications of neural networks in astrophysics, which were limited to solving classification problems, such as determining whether an image shows a gravitational lens or not.
The ability to sift through large amounts of data and perform complex analyses very quickly and in a fully automated fashion could transform astrophysics in a way that is much needed for future sky surveys that will look deeper into the universe – and produce more data – than ever before.
The Large Synoptic Survey Telescope (LSST), for example, whose 3.2-gigapixel camera is currently under construction at SLAC, will provide unparalleled views of the universe and is expected to increase the number of known strong gravitational lenses from a few hundred today to tens of thousands.
“We won’t have enough people to analyze all these data in a timely manner with the traditional methods,” Perreault Levasseur said. “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.”
Neural networks are inspired by the architecture of the human brain, in which a dense network of neurons quickly processes and analyzes information.
In the artificial version, the “neurons” are single computational units that are associated with the pixels of the image being analyzed. The neurons 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, and so on.
“The amazing thing is that neural networks learn by themselves what features to look for,” said KIPAC staff scientist Phil Marshall, a co-author of the paper. “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.”
But in this case, Hezaveh said, “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.”
“Neural nets have been applied to astrophysical problems in the past with mixed outcomes,” said KIPAC faculty member Roger Blandford, who was not a co-author on the paper. “But new algorithms combined with modern graphics processing units, or GPUs, can produce extremely fast and reliable results, as the gravitational lens problem tackled in this paper dramatically demonstrates. There is considerable optimism that this will become the approach of choice for many more data processing and analysis problems in astrophysics and other fields.”
Part of this work was funded by the DOE Office of Science.
Citation: Y.D. Hezaveh, L. Perreault Levasseur, P.J. Marshall, Nature, 30 August 2017 (10.1038/nature23463)
Press Office Contact: Andrew Gordon, agordon@slac.stanford.edu, (650) 926-2282
SLAC is a multi-program laboratory exploring frontier questions in photon science, astrophysics, particle physics and accelerator research. Located in Menlo Park, California, SLAC is operated by Stanford University for the U.S. Department of Energy Office of Science. To learn more, please visit www.slac.stanford.edu.
SLAC National Accelerator Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.
“Analyses that typically take weeks to months to complete, that require the input of experts and that are computationally demanding, can be done by neural nets within a fraction of a second, in a fully automated way and, in principle, on a cell phone’s computer chip,” said postdoctoral fellow Laurence Perreault Levasseur, a co-author of a study published today in Nature.
KIPAC scientists have for the first
time used artificial neural networks to analyze complex distortions in
spacetime, called gravitational lenses, demonstrating that the method is
10 million times faster than traditional analyses. (Greg Stewart/SLAC
National Accelerator Laboratory)
Lightning Fast Complex AnalysisThe team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, 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, that’s closer to us. The distortions provide important clues about how mass is distributed in space and how that distribution changes over time – properties linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that’s accelerating the expansion of the universe.
Until now this type of analysis has been a tedious process that involves comparing actual images of lenses with a large number of computer simulations of mathematical lensing models. This can take weeks to months for a single lens.
But with the neural networks, the researchers were able to do the same analysis in a few seconds, which they demonstrated using real images from NASA’s Hubble Space Telescope and simulated ones.
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. In a separate paper, submitted to The Astrophysical Journal Letters, the team reports how these networks can also determine the uncertainties of their analyses.
KIPAC
researcher Phil Marshall explains the optical principles of
gravitational lensing using a wineglass. (Brad Plummer/SLAC National
Accelerator Laboratory)
Prepared for Data Floods of the Future“The neural networks we tested – three publicly available neural nets and one that we developed ourselves – were able to determine the properties of each lens, including how its mass was distributed and how much it magnified the image of the background galaxy,” said the study’s lead author Yashar Hezaveh, a NASA Hubble postdoctoral fellow at KIPAC.
This goes far beyond recent applications of neural networks in astrophysics, which were limited to solving classification problems, such as determining whether an image shows a gravitational lens or not.
The ability to sift through large amounts of data and perform complex analyses very quickly and in a fully automated fashion could transform astrophysics in a way that is much needed for future sky surveys that will look deeper into the universe – and produce more data – than ever before.
The Large Synoptic Survey Telescope (LSST), for example, whose 3.2-gigapixel camera is currently under construction at SLAC, will provide unparalleled views of the universe and is expected to increase the number of known strong gravitational lenses from a few hundred today to tens of thousands.
“We won’t have enough people to analyze all these data in a timely manner with the traditional methods,” Perreault Levasseur said. “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.”
KIPAC researchers used images of
strongly lensed galaxies taken with the Hubble Space Telescope to test
the performance of neural networks, which promise to speed up complex
astrophysical analyses tremendously. (Yashar Hezaveh/Laurence Perreault
Levasseur/Phil Marshall/Stanford/SLAC National Accelerator Laboratory;
NASA/ESA)
A Revolutionary ApproachNeural networks are inspired by the architecture of the human brain, in which a dense network of neurons quickly processes and analyzes information.
In the artificial version, the “neurons” are single computational units that are associated with the pixels of the image being analyzed. The neurons 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, and so on.
“The amazing thing is that neural networks learn by themselves what features to look for,” said KIPAC staff scientist Phil Marshall, a co-author of the paper. “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.”
But in this case, Hezaveh said, “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.”
Scheme of an artificial neural
network, with individual computational units organized into hundreds of
layers. Each layer searches for certain features in the input image (at
left). The last layer provides the result of the analysis. The
researchers used particular kinds of neural networks, called
convolutional neural networks, in which individual computational units
(neurons, gray spheres) of each layer are also organized into 2-D slabs
that bundle information about the original image into larger
computational units. (Greg Stewart/SLAC National Accelerator Laboratory)
Although the KIPAC scientists ran
their tests on the Sherlock high-performance computing cluster at the
Stanford Research Computing Center, they could have done their
computations on a laptop or even on a cell phone, they said. In fact,
one of the neural networks they tested was designed to work on iPhones.“Neural nets have been applied to astrophysical problems in the past with mixed outcomes,” said KIPAC faculty member Roger Blandford, who was not a co-author on the paper. “But new algorithms combined with modern graphics processing units, or GPUs, can produce extremely fast and reliable results, as the gravitational lens problem tackled in this paper dramatically demonstrates. There is considerable optimism that this will become the approach of choice for many more data processing and analysis problems in astrophysics and other fields.”
Part of this work was funded by the DOE Office of Science.
-Written by Manuel Gnida
Citation: Y.D. Hezaveh, L. Perreault Levasseur, P.J. Marshall, Nature, 30 August 2017 (10.1038/nature23463)
Press Office Contact: Andrew Gordon, agordon@slac.stanford.edu, (650) 926-2282
SLAC is a multi-program laboratory exploring frontier questions in photon science, astrophysics, particle physics and accelerator research. Located in Menlo Park, California, SLAC is operated by Stanford University for the U.S. Department of Energy Office of Science. To learn more, please visit www.slac.stanford.edu.
SLAC National Accelerator Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.
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