For another perspective, check out this article in Symmetry Magazine, "Neural networks meet space" [1]. > "Analyses that typically take weeks to months to comp

For another perspective, check out this article in Symmetry Magazine, "Neural networks meet space" [1]. > "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,” says 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." [1] https://www.symmetrymagazine.org/article/neural-networks-meet-space https://www.symmetrymagazine.org/article/neural-networks-mee...