麻豆淫院

A team from the University of Bern and the National Center of Competence in Research (NCCR) PlanetS has developed a machine-learning model that predicts potential planetary systems with Earth-like planets. The model could significantly accelerate and thus revolutionize the future search for habitable planets in the universe.

The search for Earth-like exoplanets鈥攑lanets orbiting stars other than our sun鈥攊s a central topic in today's planetary research, because extraterrestrial life is most likely to be found there. Researchers at the University of Bern have now developed an innovative machine learning model that identifies planetary systems that could potentially harbor Earth-like planets.

The entire team behind the findings is, or was at the time of the study, affiliated with the University of Bern and a member of the NCCR PlanetS. The first author, Dr. Jeanne Davoult, who is now a postdoctoral researcher at DLR (Deutsches Zentrum f眉r Luft- und Raumfahrt) in Berlin, studies exoplanet populations and developed the model as part of her doctoral thesis at the Space Research and Planetary Sciences Division (WP) of the 麻豆淫院ics Institute of the University of Bern.

Prof. Dr. Yann Alibert, co-director of the Center for Space and Habitability (CSH), and Romain Eltschinger, also a Ph.D. student at the CSH, made significant contributions to the study, which has just been in the journal Astronomy & Astrophysics.

Training with data from the Bern Model

A machine learning model is a statistical tool that is trained with data to recognize certain types of patterns and make predictions. Dr. Davoult explains, "Our model is based on an algorithm that I developed and that was trained to recognize and classify planetary systems that harbor Earth-like planets."

The model builds on previous studies to infer a correlation between the presence or absence of an Earth-like planet and the properties of its system.

The algorithm was trained and tested with data from the so-called Bern Model of Planet Formation and Evolution. "The Bern Model can be used to make statements about how planets were formed, how they have evolved, and which types of planets develop under certain conditions in a protoplanetary disk," explains co-author Dr. Alibert.

Since 2003, the Bern Model has been continuously developed at the University of Bern. "The Bern Model is one of the only models worldwide that offers such a wealth of interrelated physical processes and enables a study like the current one to be carried out," Dr. Alibert continues.

99% accuracy of the new model

The algorithm of the new machine learning model was trained and tested using data on synthetic planetary systems from the Bern Model. "The results are impressive: the algorithm achieves precision values of up to 0.99, which means that 99% of the systems identified by the machine learning model have at least one Earth-like planet," says Dr. Davoult.

The model was then applied to actually observed planetary systems. "The model identified 44 systems that are highly likely to harbor undetected Earth-like planets. A further study confirmed the theoretical possibility for these systems to host an Earth-like planet," Dr. Davoult explains.

More efficient search for habitable planets

As part of his master's thesis, Eltschinger, the co-author of the study, contributed to the further development of the machine learning model, allowing it to be used in an even wider range of scenarios.

He says, "These results are important for the scientific community, and particularly for future space missions such as PLATO or future mission concepts like LIFE, which will be dedicated to the discovery and characterization of small, cold planets."

The use of this machine-learning model to search more specifically for Earth-like planets could minimize search times and maximize the number of discoveries. "This is a significant step in the search for planets with conditions favorable to life and, ultimately, in the search for life in the universe," concludes Dr. Alibert.