Wouldn’t discovering life on other worlds be made easier if we knew the exact locations to search? However, opportunities to collect samples or access remote sensing instruments are limited. A recent study, published in Nature Astronomy and led by SETI Institute Senior Research Scientist Kim Warren-Rhodes, brings us one step closer to finding extraterrestrial life. The interdisciplinary study maps the scarce life forms hidden within salt domes, rocks, and crystals at Salar de Pajonales, located at the boundary of the Chilean Atacama Desert and Altiplano.

Warren-Rhodes teamed up with Michael Phillips from the Johns Hopkins Applied Physics Lab and Freddie Kalaitzis from the University of Oxford to train a machine-learning model that could recognize patterns and rules associated with the distribution of life forms. This model was designed to predict and identify similar distributions in untrained data. By combining statistical ecology with AI/ML, the scientists achieved a remarkable outcome: the ability to locate and detect biosignatures up to 87.5% of the time, compared to just 10% with a random search. This also reduced the search area by as much as 97%.

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