In computer simulations, researchers have discovered that a neural network can infer the amount of matter in a whole universe by studying just one galaxy.
) project. These universes had a range of compositions, containing between 10 percent and 50 percent matter with the rest made up of dark energy, which drives the universe to expand faster and faster. As the simulations ran, dark matter and visible matter swirled together into galaxies. The simulations also included rough treatments of complicated events like supernovas and jets that erupt from supermassive black holes.
, an expert in simulating galaxy formation at the Max Planck Institute for Astrophysics who was not involved in the research. The team spent half a year trying to understand how the neural network had gotten so wise. They checked to make sure the algorithm hadn’t just found some way to infer the density from the coding of the simulation rather than the galaxies themselves. “Neural networks are very powerful, but they are super lazy,” Villaescusa-Navarro said.
The neural network found a much more precise and complicated relationship between 17 or so galactic properties and the matter density. This relationship persists despite galactic mergers, stellar explosions and black hole eruptions.
“I’m very impressed by the possibilities, but one needs to avoid being too carried away,” Springel said.
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