Artificial intelligence analyzes gravitational lenses 10 million times faster
DOE/SLAC National Accelerator Laboratory
SLAC and Stanford researchers demonstrate that brain-mimicking ‘neural networks’ can revolutionize the way astrophysicists analyze their most complex data 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. 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. 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. 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 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.