A group of scientists may have found a new way to do science.

The composition of the universe is usually determined by observing as much of it as possible. The researchers found that a machine learning program can scrutinize a single simulation of a universe and predict its makeup, similar to analyzing a random grain of sand under a microscope. The machines seem to have found a pattern that could one day allow astronomy to draw conclusions about the real universe by studying its building blocks.

The idea of measuring millions of galaxies is completely different, according to Francisco Villaescusa-Navarro, the lead author of the work. It's amazing that this works.

It wasn't supposed to. The find grew out of an exercise Villaescusa-Navarro gave to Jupiter Ding, a Princeton University undergraduate: build a neural network that can estimate a couple of cosmological attributes. The assignment was intended to show Ding how to use machine learning. The computer nailed the density of matter.

It was hard for me to believe that the student made a mistake.

The results of the investigation were submitted for publication in a January 6 preprint. The researchers looked at 2,000 digital universes generated by the Cosmology and Astrophysics with Machine Learning Simulations project. The universes had a range of compositions, with 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 universe consists of roughly one-third dark and visible matter and two-thirds dark energy. The simulations showed dark matter and visible matter in the sky. The simulations included treatments of complicated events like jets that erupt from black holes.

The neural network studied nearly 1 million simulations. It knew the size, composition, mass, and more than a dozen other characteristics of each galaxy from its godlike perspective. The density of matter in the parent universe is related to this list of numbers.

It succeeded. The neural network was able to predict the density of matter within 10 percent when tested on thousands of fresh galaxies.

It was surprising to me that a single galaxy could have a density of 10 percent or so.

Researchers were astonished by the performance of the algorithm. Some form all in one go and others grow by eating their neighbors. Most of the visible matter in dwarf galaxies is ejected by black holes and supernovas. The density of matter in the universe was kept close tabs on by every galaxy.

One interpretation is that the universe and/or galaxies are simpler than we had thought. There are unrecognized flaws in the simulations.