“While we have a rough idea of where to look for the remains of microbial life on Mars, narrowing down the search to the scale of tiny habitats is a much more difficult task.”

Scientists have developed a new method for optimizing the search for alien life on Mars by training artificial intelligence (AI) to identify sites that are most likely to contain “biosignatures,” or signs of life. The AI framework was trained to find biosignatures in an extreme Chilean environment that is exposed to high levels of radiation and extremely cold temperatures, conditions that are similar to those on Mars. (Vice)

The AI tool was able to improve the probability of detecting biosignatures in this harsh environment up to 87.5%, making it roughly nine times more effective than random searches for signs of life. The researchers used aerial and ground observations to map out the distribution of photosynthetic microorganisms that live in this environment and trained neural networks to predict the odds of biosignatures using geological parameters.

The deep learning approach was able to predict probabilities of biosignature detection at an impressive level of 56.9 to 87.5 percent, making it a powerful tool to expedite the search and detection of biosignatures in terrestrial analogues.

The researchers believe this “proof-of-concept” study offers a new path forward in the search for aliens on Mars and other worlds, although they noted that many improvements could be made to optimize the tool further, such as developing a “library” of data about biosignature distribution on Earth that could be applied to extraterrestrial environments of all kinds.

Ultimately, the framework may also have applications to other astrobiology targets, such as the surface of Titan, the plumes of Enceladus or the ice cover of Europa.

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