The search to detect signs of extraterrestrial life beyond our planet has received a promising boost thanks to a machine learning program.
Developed to differentiate between living and non-living substancesthis program has demonstrated impressive success in identifying biological products from those of non-living origin, according to a study published in Proceedings of the National Academy of Sciences.
Although they are currently trained in forms of terrestrial lifescientists are excited about the possibility of deploying it to analyze samples from extraterrestrial environments, including Mars.
Challenges in recognizing extraterrestrial life
Astrobiologists have long grappled with the challenge of distinguishing extraterrestrial life forms if they encounter them.. Concerns range from inadvertently killing extraterrestrial life, as some suspect happened with the Viking lander on Mars, to simply not recognizing it due to unknown characteristics.
To address these concerns, Professor Robert Hazen of the Carnegie Institution led a team that designed a machine learning program with the ability to differentiate life from non-life based on chemical patterns.
“We are asking a fundamental question; Is there something fundamentally different about the chemistry of life compared to the chemistry of the inanimate world? Hazen said in a statement.
The researchers performed pyrolysis gas chromatography mass spectrometry (GCMS) on 134 carbon-rich samples. GCMS involves heating materials without oxygen and then analyzing the molecules that make them up en masse.
These samples included 59 of biological originfrom shells and leaves to crude oil, and 75 non-biological samples, such as those from meteorites rich in carbon and laboratory-made amino acids.
The results of the study were presented at the Goldschmidt Geochemistry Conference and have now been published. The machine learning program achieved a remarkable More than 90% success rate in distinguishing biological from non-biological samples.In particular, its accuracy is expected to improve further with more sample data, following machine learning principles.
Interestingly, the program exceeded expectations by identifying three distinct populations: abiotic, living biotic, and fossil biotic. It could distinguish fossil samples from more recent biological samples, demonstrating the depth of its analysis.
Professor Hazen believes this routine analytical method could revolutionize search for extraterrestrial life. By applying smart sensors equipped with this technology to robotic spacecraft, landers and rovers, we could search for signs of life before the samples return to Earth.
Existing data from Martian landers and rovers could even be re-evaluated for possible evidence of ancient life.
Keep reading:
· UFOs: What the new report published by NASA says
· A planet in a distant galaxy could host life: NASA
