Where To Find The Next Earth
A team from the University of Bern and the National Centre 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 – planets orbiting stars other than our Sun – is 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 to 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 für 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 Physics Institute of the University of Bern. Prof. Dr. Yann Alibert, co-director of the Centre for Space and Habitability (CSH), and Romain Eltschinger, also a PhD student at the CSH, made significant contributions to the study, which has just been published in the journal Astronomy & Astrophysics.
Training with data from the renowned Bern Model
A machine learning model is a statistical tool that is trained with data to recognize certain types of patterns and make predictions. Jeanne 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 disc,” explains co-author Yann Alibert. Since 2003, the Bern Model has been continuously developed at the University of Bern (see also the infobox below). “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,” 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 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”, Davoult explains.
More efficient search for habitable planets
As part of his master’s thesis, the co-author of the study Romain Eltschinger 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 favourable to life and, ultimately, for the search of life in the universe,” concludes Alibert.
Publication details:
Earth-like planets Predictor: A Machine Learning Approach. By Jeanne Davoult, Romain Eltschinger, and Yann Alibert, In: Astronomy & Astrophysics
https://www.aanda.org/10.1051/0004-6361/202452434
DOI: 10.1051/0004-6361/202452434
Bern Model of Planet Formation and Evolution
Statements can be made about how a planet was formed and how it has evolved using the “Bern Model of Planet Formation and Evolution”. The Bern model has been continuously developed at the University of Bern since 2001. Insights into the manifold processes involved in the formation and evolution of planets are integrated into the model. These are, for example, sub models of accretion (growth of a planet’s core) or of how planets interact gravitationally and influence each other, and of processes in the protoplanetary disks in which planets are formed. The model is also used to create so-called population syntheses, which show how often planets form in a protoplanetary disk under certain conditions.
Bernese space exploration: With the world’s elite since the first moon landing When the second man, “Buzz” Aldrin, stepped out of the lunar module on July 21, 1969, the first task he did was to set up the Bernese Solar Wind Composition experiment (SWC) also known as the “solar wind sail” by planting it in the ground of the moon, even before the American flag. This experiment, which was planned, built and the results analyzed by Prof. Dr. Johannes Geiss and his team from the Physics Institute of the University of Bern, was the first great highlight in the history of Bernese space exploration.
Ever since Bernese space exploration has been among the world’s elite, and the University of Bern has been participating in space missions of the major space organizations, such as ESA, NASA, and JAXA. With CHEOPS the University of Bern shares responsibility with ESA for a whole mission. In addition, Bernese researchers are among the world leaders when it comes to models and simulations of the formation and development of planets.
The successful work of the Space Research and Planetary Sciences Division (WP) from the Physics Institute of the University of Bern was consolidated by the foundation of a university competence center, the Center for Space and Habitability (CSH). The Swiss National Fund also awarded the University of Bern the National Center of Competence in Research (NCCR) PlanetS, which it manages together with the University of Geneva.
2025/04/09
Astrobiology
