Thousands of universes were born in a computer, all to tell us about our own. Each one has its own galaxy clusters, arranged by the laws of gravity like peas in a jar shaken with varying force. From how the peas settle, you can tell what kind of hand shook the jar. Same here: from the pattern of clusters, scientists want to determine how much dark matter the universe holds and how quickly it clumped together.

Previously, all we could extract from these patterns were a couple of numbers — how many clusters there are in total and how much they like to sit at certain distances. That's like judging a painting by its average color and brushstroke count. Now, the neural network looks at the whole picture: a three-dimensional map where each point is a galaxy cluster. And it learns from thirty-two thousand such maps, generated under different scenarios.

The results are encouraging: the new approach makes ten percent smaller errors in estimating matter density than the old method, and twenty percent smaller errors in estimating clumpiness. If the neural network is also given the brightness of each cluster, accuracy improves by another factor of two. Brightness here is like pea size: it also depends on how much dark matter surrounds it.

In this way, computer universes become a textbook for reading our own. The better the textbook, the more precisely we learn what the cosmos is made of and where it is heading.