Latest news with #BernModelofPlanetFormationandEvolution
Yahoo
21-04-2025
- Science
- Yahoo
An AI Identifies Where All Those Planets That Could Host Life Are Hiding
Researchers in Switzerland have built an AI model to uncover potentially habitable worlds that are hiding from view. As detailed in a new study published in the journal Astronomy and & Astrophysics, the machine learning algorithm identified 44 star systems that it suspects harbor Earth-like exoplanets we haven't detected yet, in a promising demonstration of an approach that could accelerate the search for planets teeming with life. It hasn't outright confirmed that the Earth-like planets are actually there — but it's teed up astronomers to investigate those stellar neighborhoods in the future. In simulations, the model achieved an impressive precision value of up to 0.99, meaning that 99 percent of the systems identified 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," co-author Dr. Yann Alibert, codirector of the University of Bern's Centre for Space and Habitability, said in a statement, as quoted by Forbes. "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." Exoplanets are notoriously difficult to spot, because they're tiny compared to stars and produce little light of their own. So far, scientists have confirmed the existence of just over 5,800 planets outside our solar system — and the data we have on most of these is scant. That doesn't give a lot of material to train a pattern-seeking algorithm on, which require huge data sets. Instead, the astronomers fed their model synthetic planetary systems generated with the Bern Model of Planet Formation and Evolution, which comprehensively simulates the development of hypothetical planets as far back to their inception from a protoplanetary disc. "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 said in a statement about the research. During these tests, the AI model revealed that the strongest indicators of an Earth-like planet could be found in a system's innermost detectable planet, particularly its mass and orbital period, the researchers wrote in the study. From there, the team applied the machine learning algorithm to a sample of nearly 1,600 systems with at least one known planet and either a G-type, K-type, or M-type star, with G-types being Sun-like stars, and the remaining two classifications describing stars that are smaller and cooler. That revealed that nearly four dozen of them likely harbor an Earth-like world. But the model isn't infallible. It hasn't reproduced certain characteristics of star systems that astronomers have observed, such as the strong correlation between so-called Super Earths and Cold Jupiters, which often appear together around Sun-like stars, the authors note. And the synthetic planets tend to be found closer to their stars than real ones. Still, it doesn't need to be perfect: anything to narrow down astronomers' hunt for Earth's cousins through the the unfathomably vast cosmos could be a gamechanger. More on exoplanets: James Webb Space Telescope Captures Images of Individual Planets in Distant Star System
Yahoo
20-04-2025
- Science
- Yahoo
An AI Identifies Where All Those Planets That Could Host Life Are Hiding
Researchers in Switzerland have built an AI model to uncover potentially habitable worlds that are hiding from view. As detailed in a new study published in the journal Astronomy and & Astrophysics, the machine learning algorithm identified 44 star systems that it suspects harbor Earth-like exoplanets we haven't detected yet, in a promising demonstration of an approach that could accelerate the search for planets teeming with life. It hasn't outright confirmed that the Earth-like planets are actually there — but it's teed up astronomers to investigate those stellar neighborhoods in the future. In simulations, the model achieved an impressive precision value of up to 0.99, meaning that 99 percent of the systems identified 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," co-author Dr. Yann Alibert, codirector of the University of Bern's Centre for Space and Habitability, said in a statement, as quoted by Forbes. "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." Exoplanets are notoriously difficult to spot, because they're tiny compared to stars and produce little light of their own. So far, scientists have confirmed the existence of just over 5,800 planets outside our solar system — and the data we have on most of these is scant. That doesn't give a lot of material to train a pattern-seeking algorithm on, which require huge data sets. Instead, the astronomers fed their model synthetic planetary systems generated with the Bern Model of Planet Formation and Evolution, which comprehensively simulates the development of hypothetical planets as far back to their inception from a protoplanetary disc. "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 said in a statement about the research. During these tests, the AI model revealed that the strongest indicators of an Earth-like planet could be found in a system's innermost detectable planet, particularly its mass and orbital period, the researchers wrote in the study. From there, the team applied the machine learning algorithm to a sample of nearly 1,600 systems with at least one known planet and either a G-type, K-type, or M-type star, with G-types being Sun-like stars, and the remaining two classifications describing stars that are smaller and cooler. That revealed that nearly four dozen of them likely harbor an Earth-like world. But the model isn't infallible. It hasn't reproduced certain characteristics of star systems that astronomers have observed, such as the strong correlation between so-called Super Earths and Cold Jupiters, which often appear together around Sun-like stars, the authors note. And the synthetic planets tend to be found closer to their stars than real ones. Still, it doesn't need to be perfect: anything to narrow down astronomers' hunt for Earth's cousins through the the unfathomably vast cosmos could be a gamechanger. More on exoplanets: James Webb Space Telescope Captures Images of Individual Planets in Distant Star System
Yahoo
16-04-2025
- Science
- Yahoo
How artificial intelligence is helping scientists hunt for alien Earths
When you buy through links on our articles, Future and its syndication partners may earn a commission. A machine-learning algorithm trained on synthetic planetary systems has been let loose — and in the process has identified nearly four dozen real stars that have a high probability of hosting a rocky planet in their habitable zone. "The model identified 44 systems that are highly likely to harbor undetected Earth-like planets," said Jeanne Davoult, an astronomer at the German Aerospace Agency DLR, in a statement. "A further study confirmed the theoretical possibility for these systems to host an Earth-like planet." Often, "Earth-like" worlds — Earth-like in the sense that they have a similar mass to our planet and reside in their star's habitable zone — are found by chance, often in huge surveys that watch thousands of stars for transiting planets. However, astronomers would like to even the odds of finding Earth-size habitable-zone planets, and hence require a more targeted means of finding candidate stars. This is what led Davoult to develop the algorithm while she was at the University of Bern in Switzerland. Like all models based on machine-learning algorithms that learn to identify patterns and make predictions based on where the algorithm sees those patterns, it had to be trained on data. The problem, however, is that although nearly 6,000 exoplanets have been discovered so far, the information that we have on these worlds is patchy. And in general, even 6,000 worlds is not enough to train the algorithm. So, Davoult and her colleagues at the University of Bern, Romain Eltschinger and Yann Alibert, turned to another model that is able to simulate worlds based on everything we know about planetary systems. The Bern Model of Planet Formation and Evolution has been in continuous development at the University of Bern since 2003, and is constantly undergoing improvements as more data and theoretical models become available. "The Bern Model can be used to make statements about how planets were formed, how they evolved and which types of planets develop under certain conditions in a protoplanetary disk," Alibert said in the statement. "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." The Bern Model spat out 53,882 simulated planetary systems around three different types of stars: G-type stars like our sun, red dwarfs with about half the mass of the sun, and a second group of red dwarfs with just a fifth of a solar mass. The algorithm set about searching these simulated planetary systems for patterns or correlations, connecting the presence or absence of an Earth-size habitable-zone planet with various architectures of the planetary systems. Some correlations are more evident than others. For example, there's a correlation between the existence of an inner rocky planet co-inhabiting a system with an outer gas giant. This is the same architecture that our solar system has, with the rocky planets closer to the sun than the gas giants. On the flip-side, there's an anti-correlation between hot Jupiters, which are gas giants close to their sun, and "peas-in-a-pod" planets, which are strings of rocky planets of similar mass and orbital spacing that have been found around some red dwarf stars such as TRAPPIST-1 and Barnard's Star. Because a hot Jupiter is a gas giant that formed farther out from its star and then migrated inwards, knocking any planets in its path out of the way, we would not expect to find a hot Jupiter alongside such orderly rocky planets. But there are deeper correlations too, which were identified by Davoult in earlier research. In particular, the mass, radius and orbital period of the innermost detectable planet seems to be a big signpost as to whether a system hosts an Earth-size, temperate planet or not. For instance, Davoult found that around G-type stars like our sun, the existence of an Earth-sized habitable zone planet seems more probable if the radius of the innermost detectable planet is greater than 2.5 times the radius of Earth, or if it has an orbital period greater than 10 days. Armed with the knowledge of these correlations, the algorithm was successfully trained on the simulated data. "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," said Davoult. Confident in the algorithm's ability to recognize correlations, it was then applied to real observations, providing the 44 candidate planetary systems in which there is a high probability that an Earth-size planet exists in the habitable zone of its star. Astronomers can now follow up on these targets, rather than searching stars blindly. Related Stories: — How climate change could make Earth's space junk problem even worse — Large alien planets may be born in chaos, NASA's retired exoplanet-hunter finds — Nearby exoplanet could offer clues about atmospheres around hot, rocky alien worlds The algorithm will really prove its worth in the future. The European Space Agency's PLATO mission is expected to discover many thousands of transiting planets. By applying the algorithm to PLATO's discoveries, it should be able to narrow down the many thousands of systems to the few that have a higher chance of supporting an Earth-like planet, allowing astronomers to find them more quickly and efficiently. "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," said Alibert. The findings are published in the April 2025 issue of the journal Astronomy & Astrophysics.
Forbes
16-04-2025
- Science
- Forbes
Artificial Intelligence Just Identified 44 Earth-Like Planets — What To Know
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. 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. 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. 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. One Community. Many Voices. Create a free account to share your thoughts. Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space. In order to do so, please follow the posting rules in our site's Terms of Service. We've summarized some of those key rules below. Simply put, keep it civil. Your post will be rejected if we notice that it seems to contain: User accounts will be blocked if we notice or believe that users are engaged in: So, how can you be a power user? Thanks for reading our community guidelines. Please read the full list of posting rules found in our site's Terms of Service.
