A group of scientists may have stumbled upon a radical new way to do cosmology.
Cosmologists usually determine the composition of the universe by observing as much of it as possible. But these researchers have found that a machine learning algorithm can scrutinize a single simulated galaxy and predict the overall makeup of the digital universe in which it exists—a feat analogous to analyzing a random grain of sand under a microscope and working out the mass of Eurasia. The machines appear to have found a pattern that might someday allow astronomers to draw sweeping conclusions about the real cosmos merely by studying its elemental building blocks.
“This is a completely different idea,” said Francisco Villaescusa-Navarro, a theoretical astrophysicist at the Flatiron Institute in New York and lead author of the work. “Instead of measuring these millions of galaxies, you can just take one. It’s really amazing that this works.”
It wasn’t supposed to. The improbable find grew out of an exercise Villaescusa-Navarro gave to Jupiter Ding, a Princeton University undergraduate: Build a neural network that, knowing a galaxy’s properties, can estimate a couple of cosmological attributes. The assignment was meant merely to familiarize Ding with machine learning. Then they noticed that the computer was nailing the overall density of matter.
“I thought the student made a mistake,” Villaescusa-Navarro said. “It was a little bit hard for me to believe, to be honest.”
The results of the investigation that followed appeared in a January 6 preprint that has been submitted for publication. The researchers analyzed 2,000 digital universes generated by the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) 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. (Our actual cosmos consists of roughly one-third dark and visible matter and two-thirds dark energy.) 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.
Ding’s neural network studied nearly 1 million simulated galaxies within these diverse digital universes. From its godlike perspective, it knew each galaxy’s size, composition, mass, and more than a dozen other characteristics. It sought to relate this list of numbers to the density of matter in the parent universe.
It succeeded. When tested on thousands of fresh galaxies from dozens of universes it hadn’t previously examined, the neural network was able to predict the cosmic density of matter to within 10 percent. “It doesn’t matter which galaxy you are considering,” Villaescusa-Navarro said. “No one imagined this would be possible.”
“That one galaxy can get [the density to] 10 percent or so, that was very surprising to me,” said Volker Springel, an expert in simulating galaxy formation at the Max Planck Institute for Astrophysics who was not involved in the research.
The algorithm’s performance astonished researchers because galaxies are inherently chaotic objects. Some form all in one go, and others grow by eating their neighbors. Giant galaxies tend to hold onto their matter, while supernovas and black holes in dwarf galaxies might eject most of their visible matter. Still, every galaxy had somehow managed to keep close tabs on the overall density of matter in its universe.
One interpretation is “that the universe and/or galaxies are in some ways much simpler than we had imagined,” said Pauline Barmby, an astronomer at Western University in Ontario. Another is that the simulations have unrecognized flaws.
