Earth-like planet predictor: using AI to predict planet detection

Kurzfassung

The search for Earth-like exoplanets, planets similar to the Earth and 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 DLR Berlin and the University of Bern have now developed an innovative machine learning model that identifies planetary systems that could potentially harbor Earth-like planets. The model could significantly accelerate and thus revolutionize the future search for habitable planets in the universe. A machine learning model is a statistical tool that is trained with data to recognize certain types of patterns and make predictions. The algorithm was trained and tested with data from the so-called “Bern Model of Planet Formation and Evolution”. The model was then applied to actually observed planetary systems and 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. 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.