At the Large Hadron Collider, protons smash into each other like two trucks full of confetti. The debris, dozens of new particles, scatters in all directions. The detector records their tracks, and then the real work begins: piecing together a complete picture of the collision from those tracks.

Previously, this was done by a set of rules devised by physicists. Each particle type was processed separately, according to its own laws. It worked, but slowly and not always accurately. Now, instead of a set of rules, there's a single neural network. It was trained on millions of simulated collisions, and it learned on its own to reconstruct the entire picture.

The neural network runs on a graphics card — the same kind that renders graphics in games. It takes 20 milliseconds per collision. The old method took 110 milliseconds. And for jets, streams of particles flying in one direction, the accuracy of energy measurement improved by 10–20 percent.

This approach is like a translator who used to translate word by word using a dictionary, but now understands the whole phrase. As a result, the collision picture becomes clearer, like a photograph taken in good weather.