An AI model trained on tens of thousands of simulated planetary systems achieved up to 99% precision at recognising simulations containing an Earth-mass planet in a broad temperate zone. Applied to 1,567 real systems, it identified 44 priority targets—but whether that extraordinary hit rate survives contact with the real sky remains untested.

A random-forest classifier distilled simulated planetary architectures into a 44-system observing shortlist, but its 99% precision was measured only inside the synthetic universe used to train it. The post An AI model trained on tens of thousands of simulated…