Artificial Intelligence Just Identified 44 Earth-Like Planets — What To Know
作者：Jamie Carter
Topline
A new machine learning model has predicted that there are 44 Earth-like planets in other star systems in the Milky Way galaxy, with researchers from Switzerland claiming that the algorithm at the core of the model is 99% accurate.
Artificial intelligence enables scientists to predict missing Earth-like planets in the habitable ... More zone of their host stars. The blue zone on the illustration represents the habitable zone around the star, where the temperatures allow liquid water on the surface of an Earth-like planet.
Key Facts
Researchers at the University of Bern and the National Centre of Competence in Research PlanetS (NCCR PlanetS), Switzerland, have developed a machine learning-powered Earth-like planet predictor that identifies planetary systems that could harbor Earth-like planets. The model could significantly accelerate and thus revolutionize the search for habitable planets.
These so-called exoplanets have the potential to host life. An exoplanet is any planet that orbits a star other than the sun. An Earth-like exoplanet orbits its star in the so-called habitable zone, where liquid water can exist on its surface. That's where planetary scientists think extraterrestrial life is most likely to be found.
The AI performed spectacularly well when applied to data on planetary systems with known properties and potential Earth-like planets. “The model identified 44 systems that are highly likely to harbor undetected Earth-like planets,” said Dr. Jeanne Davoult, lead author of a paper published this week in Astronomy & Astrophysics.
“A further study confirmed the theoretical possibility for these systems to host an Earth-like planet,” said Davoult, who developed the model as part of her doctoral thesis at the Space Research and Planetary Sciences Division of the Physics Institute of the University of Bern.
According to Davoult, 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.”
“It’s one of the few models worldwide with this level of complexity and depth, enabling predictive studies like ours,” said co-author Dr. Yann Alibert, co-director of the University of Bern’s Centre for Space and Habitability. “This is a significant step in the search for planets with conditions favorable to life and, ultimately, for the search for life in the universe.”
It’s thought that the new model will reduce the time needed to sift through star systems with the potential for Earth-like planets, allowing astronomers to point their telescopes only at the most promising targets, thus increasing the chances of finding life beyond Earth.
What The Ai Was Trained On
Machine learning models are trained using data to recognize certain types of patterns so they can make predictions. This model is based on a unique new algorithm developed to recognize and classify planetary systems that harbor Earth-like planets. Based on correlating the presence or absence of an Earth-like planet and the properties of a star system, the algorithm was trained on and tested with data from the 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,” said Alibert.
Background
In March, scientists concluded the search for planets around Barnard’s Star, the second-nearest star system to Earth, announcing four worlds. A red dwarf star in the constellation Ophiuchus, just six light-years from the solar system, Barnard’s Star is now confirmed to host four planets about 20-30% of the mass of Earth that orbit their star in a few days. That puts them firmly outside the habitable zone and is likely too hot to support life.
Further Reading
ForbesFound: Four Rocky Planets Orbiting Our Closest Lone StarBy Jamie Carter
ForbesNew Theory On Why We Are Alone In The Cosmos—Tectonic PlatesBy Jamie Carter
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