University of Bern AI model identifies 44 star systems that could host Earth-like planets
A team at the University of Bern and Switzerland’s National Centre of Competence in Research PlanetS built a machine‑learning model that scans known exoplanet systems for clues that an undiscovered Earth‑like world may be present. Using a random‑forest classifier trained on tens of thousands of synthetic planetary systems, the model achieved up to 99 % precision on simulated data.
Key points
- AI model trained on synthetic planetary systems reached 99 % precision on simulated data
- Applied to real observations, it identified 44 star systems as potential hosts for Earth‑like planets
- Stability analysis found orbital space for additional planets in 42 of the 44 systems
When applied to 1,567 observed systems with at least one measured planet, the algorithm flagged 51 candidates; after removing seven binary‑star cases, 44 systems remained as the proposed target list. A preliminary stability check found suitable orbital space in 42 of those systems. The researchers stress that the 99 % figure reflects tests on simulated data, not confirmed detections, and that real‑world observations will be needed to validate the predictions.
The work builds on earlier studies linking planetary architecture to the presence of temperate terrestrial planets. The authors note limitations in the synthetic model and detection filter, and suggest the list could help prioritize follow‑up searches for missions such as PLATO and the proposed LIFE interferometer.
AI finds 44 star systems that could hide Earth-like planets
thebrighterside.news · 5 October 2026
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