The new research uses information from life found in inhospitable locations on Earth like the salt formations in Salar de Pajonales, a salt flat region in the Atacama desert in Chile. The researchers mapped life found here, then trained a machine learning model on the distribution of life across the region to help it recognize locations for potential life to be found at other locations.
This machine learning model helped identify biosignature more precisely, meaning only a smaller area had to be searched.
“Our framework allows us to combine the power of statistical ecology with machine learning to discover and predict the patterns and rules by which nature survives and distributes itself in the harshest landscapes on Earth,” said lead researcher Kim Warren-Rhodes of the SETI Institute. “We hope other astrobiology teams adapt our approach to mapping other habitable environments and biosignatures.”
The long-term plan for this kind of research is that it could be used by scientists who are planning missions on other planets like Mars, to help them narrow down the areas in which they should be searching. It could use data collected by orbiters which take images of a planet’s surface, then select likely locations from that data — and that could help uncover indications if there was in fact life on Mars millions of years ago.
“With these models, we can design tailor-made roadmaps and algorithms to guide rovers to places with the highest probability of harboring past or present life—no matter how hidden or rare,” Warren-Rhodes said.
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