This image taken by NASA's Perseverance rover on Aug. 6, 2021, shows the hole drilled in a Martian rock in preparation for the rover's first attempt to collect a sample.
As of now, life is only known to exist on Earth in the vast universe.
Nonetheless, the quest for extraterrestrial life forms has been a significant focus of space exploration. Various space missions continue to broaden our understanding of where life beyond Earth could be found.
Space scientists have identified some prime targets in our solar system where they believe alien life forms could potentially exist or be searched for, such as Mars, Jupiter’s moon Europa, and Saturn’s moon Enceladus, to mention a few.
To advance this research, a team of researchers has developed a new artificial intelligence-based method for detecting the “signs of past or present life on other planets.”
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“The search for extraterrestrial life remains one of the most tantalizing endeavors in modern science,” said Jim Cleaves, lead author at the Carnegie Institution for Science, Washington, in an official release.
AI distinguishes between biotic and abiotic samples
This innovative AI model works by distinguishing between samples of biological and abiotic origin.
The team utilized complex data from 134 carbon-rich samples, both non-living and living, to teach AI how to identify the source of a new sample.
The results showcased that AI could reveal the origin of samples with 90 percent accuracy.
The AI effectively distinguished between samples derived from living organisms, like shells, bones, insects, leaves, human hair, and preserved cells in fine-grained rock, and those with abiotic origins, such as carbon-rich meteorites.
“The implications of this new research are many, but there are three big takeaways: First, at some deep level, biochemistry differs from abiotic organic chemistry; second, we can look at Mars and ancient Earth samples to tell if they were once alive; and third, it is likely this new method could distinguish alternative biospheres from those of Earth, with significant implications for future astrobiology missions,” said Cleaves.
Several possible applications of this new technique
A notable advantage of this AI-based approach is its ability to ascertain the source of ancient carbon-bearing samples – a challenging task. This difficulty stems from organic molecules, whether from living or non-living sources, which tend to deteriorate as time passes.
Interestingly, despite substantial decay, the novel analytical method managed to detect preserved indicators of biology in certain samples dating back hundreds of millions of years.
In the near term, this AI could help analyze the data of the already collected Martian samples by the Curiosity rover’s Sample Analysis at Mars (SAM) instrument.
“We’ll need to tweak our method to match SAM’s protocols, but it’s possible that we already have data in hand to determine if there are molecules on Mars from an organic Martian biosphere,” said Cleaves.
In the future, this approach could open doors to developing advanced sensors that can be installed on robotic spacecraft, landers, and rovers. These sensors would seek out life indications even before the surface samples are brought back to Earth.
Not just this, but this new AI method can also be used on an immediate basis. It has the potential to solve long-standing scientific puzzles on Earth.
One of these instances pertains to the enigmatic source of black sediments dating back 3.5 billion years in Western Australia. These rocks have been the subject of intense debate, with some scientists arguing that they may contain our planet’s oldest fossilized microbes, while others assert that they show no signs of life.
“These results mean that we may be able to find a lifeform from another planet, another biosphere, even if it is very different from the life we know on Earth. And, if we find signs of life elsewhere, we can tell if life on Earth and other planets derived from a common or different origin,” said Robert Hazen, one of the authors.
The findings were reported in the journal Proceedings of the National Academy of Sciences.
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