A new machine learning model could point researchers toward the most promising abodes for life away from Earth.
Biosignature probability maps from convolutional neural network models and statistical ecology data. The colors in a) indicate the probability of biosignature detection. In b) is a visible image of a gypsum dome geologic feature with biosignature probability maps for various microhabitats within it.
The team discovered that their system could, by combining statistical ecology with AI, locate and detect biosignatures up to 87.5% of the time. This is in comparison to no more than a 10% success rate achieved by random searches. Additionally, the program could decrease the area needed for a search by as much as 97%, thus helping scientists significantly hone in their hunt for potential chemical traces of life, or biosignatures.
."We hope other astrobiology teams adapt our approach to mapping other habitable environments and biosignatures." "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 explained. The team chose Salar de Pajonales as a testing stage from their machine learning model because it is a suitable analog for the dry and arid landscape of modern-day. The region is a high-altitude dry salt lakebed that is blasted with a high degree of ultraviolet radiation.
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