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🪐 AI Exoplanet Discovery: AI Is Helping Astronomers Find Hidden Earths — 44 Promising Systems Identified


NewBits Digest image showing the NewBits logo in the top left and the NewBits robot in the center, used for a featured story on AI exoplanet discovery—highlighting how artificial intelligence is helping astronomers identify hidden Earth-like worlds.

Artificial intelligence is no longer just decoding language or generating images — it’s now helping astronomers hunt for Earth-like worlds beyond our solar system. In a new study published in Astronomy & Astrophysics (April 2025), scientists used a machine-learning algorithm to identify 44 real star systems that likely host rocky, potentially habitable planets. This breakthrough highlights the growing importance of AI exoplanet discovery.


🤖 How It Works: AI Exoplanet Discovery with Simulated Universes


Led by astronomer Jeanne Davoult at the German Aerospace Center (DLR), the team developed an algorithm trained not on real data — which is still too sparse — but on 53,882 simulated planetary systems generated by the Bern Model of Planet Formation and Evolution, one of the most advanced planet-formation simulators in the world.


The AI learned to spot architectural patterns in planetary systems that correlate with the presence of an Earth-size world in the star’s habitable zone.


Among the key indicators:


  • The presence of rocky inner planets with outer gas giants (like our own solar system)


  • The absence of hot Jupiters, which tend to destroy inner rocky worlds


  • Specific thresholds for radius and orbital period of the innermost detectable planets


🔍 What It Found: 44 Hidden Earths


Once trained, the model was unleashed on real star data — and flagged 44 systems that are highly likely to harbor Earth-like planets.


“The algorithm achieves precision values of up to 0.99 — meaning 99% of identified systems likely contain a habitable-zone Earth analog,” said Davoult.

🛰️ Why It’s Important


Current methods of exoplanet discovery rely heavily on broad surveys and sometimes blind luck. This new algorithm provides a targeted roadmap for astronomers to find life-friendly worlds more efficiently.


And this is just the beginning. When ESA’s PLATO mission launches later this decade, it’s expected to discover thousands of new exoplanets. AI models like this one could instantly filter and prioritize systems most likely to host habitable planets — accelerating the search for life in the universe.


“This is a significant step in the search for planets with conditions favorable to life — and ultimately, the search for life itself,” said co-author Yann Alibert.

TL;DR


A machine-learning model trained on simulated planetary systems just flagged 44 real stars likely hosting Earth-like planets. AI isn’t just helping us look for life — it’s helping us think about where to look.



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