Key Takeaways
1. A research group from the University of Bern developed a machine learning model to identify planetary systems likely to host Earth-like exoplanets, achieving 99% accuracy.
2. The model was trained using synthetic data from the “Bern Model of Planet Formation and Evolution.”
3. After training, the model identified 44 planetary systems that may contain unknown Earth-like planets.
4. The findings are crucial for upcoming space missions like ESA’s PLATO, which launches in 2026, to discover habitable exoplanets.
5. The model will help select the best targets for future missions like LIFE, aimed at studying the atmospheres of distant planets for signs of life.
A research group hailing from the University of Bern and the National Center of Competence in Research PlanetS has reached a key point in the quest for planets that may support life. Announced on April 9, 2025, this group has created a machine learning model that can accurately identify planetary systems likely to have Earth-like exoplanets. This advancement not only propels the search for habitable planets but also represents an exciting step toward finding alien life.
Development of the AI Model
The machine learning model was crafted under the direction of Dr. Jeanne Davoult during her doctoral studies at the University of Bern, with assistance from Prof. Dr. Yann Alibert and Romain Eltschinger at the Center for Space and Habitability (CSH). It underwent training using synthetic data produced by the acclaimed “Bern Model of Planet Formation and Evolution,” which mimics the physical mechanisms involved in forming planetary systems. The results are impressive: the model boasts a 99% accuracy rate in identifying systems that are very likely to host at least one Earth-like planet.
Application to Real Data
Once the training was complete, the model was tested on real observational data, leading to the identification of 44 planetary systems that might harbor previously unknown Earth-like planets. These results hold great importance for future space missions, including ESA’s PLATO and the proposed LIFE project, both of which aim to find and analyze Earth-like worlds.
PLATO (PLAnetary Transits and Oscillations of stars), scheduled to launch in 2026, will employ the transit method along with asteroseismology to discover potentially habitable exoplanets, particularly focusing on those orbiting stars similar to our Sun. The best candidates chosen by PLATO will serve as the groundwork for future missions like LIFE (Large Interferometer For Exoplanets), which plans to study the atmospheres of distant planets through infrared spectroscopy and nulling interferometry in order to search for biosignatures such as water or methane. The novel machine learning model could significantly aid in pre-selecting the most viable targets, thus improving the effectiveness and success rates of these missions.
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