Did life Exist on Mars or other Planets? AI could Reveal Soon
Researchers have unearthed a straightforward and dependable method for detecting indications of past or present life on planets, a breakthrough often referred to as the ultimate goal of astrobiology.
In the Proceedings of the National Academy of Sciences, a team of seven researchers has reported a groundbreaking development. Their artificial intelligence-based approach has shown a remarkable 90% accuracy in distinguishing between modern and ancient biological samples and those of non-biological origin. This achievement is often referred to as the holy grail of astrobiology.
Dr. Hazen, a member of the team, has described this routine analytical method as a potential game-changer in the search for extraterrestrial life and our understanding of the origin and chemistry of early life on Earth. It paves the way for the deployment of intelligent sensors on robotic spacecraft, landers, and rovers, allowing for the detection of life signs before samples return to Earth.
Unraveling Earth’s Ancient Rocks and Mars Exploration
In the immediate term, this new test could shed light on the history of enigmatic ancient rocks on Earth, and it might even apply to samples already collected by the Mars Curiosity rover’s Sample Analysis at Mars (SAM) instrument. The latter tests could be performed using an onboard analytical instrument known as “SAM” (Sample Analysis at Mars).
Lead author Jim Cleaves from the Earth and Planets Laboratory at the Carnegie Institution for Science in Washington, DC, emphasized the significance of this research. He highlighted three key takeaways: firstly, that biochemistry fundamentally differs from non-biological organic chemistry at a deep level; secondly, that it is possible to examine samples from Mars and ancient Earth to determine if they once hosted life; and thirdly, that this new method may be capable of distinguishing alternative biospheres from Earth’s, which has profound implications for future astrobiology missions.
AI Detects Molecular Patterns
The novel analytical method doesn’t rely solely on identifying specific molecules or compound groups within a sample. Instead, it utilizes artificial intelligence to detect subtle distinctions within a sample’s molecular patterns, as revealed by pyrolysis gas chromatography analysis (which separates and identifies a sample’s component parts) followed by mass spectrometry (which determines the molecular weights of those components).
A team of researchers, utilizing vast multidimensional data from molecular analyses of 134 known carbon-rich samples, employed artificial intelligence (AI) to predict the origin of new samples. Remarkably, AI achieved an accuracy rate of approximately 90% in identifying the source of samples:
- Living organisms, such as modern shells, teeth, bones, insects, leaves, rice, human hair, and cells preserved in fine-grained rock.
- Remains of ancient life altered by geological processes (e.g., coal, oil, amber, and carbon-rich fossils).
- Samples with abiotic origins, such as pure laboratory chemicals (e.g., amino acids) and carbon-rich meteorites.
This development has significant implications for astrobiology and our understanding of life’s origins. The method may be applied to detect life forms from different planets and biospheres, even if they differ substantially from Earth’s life. Additionally, the technique could differentiate recent biological samples from fossilized ones, offering new insights.
AI is instrumental in discerning subtle distinctions within molecular patterns obtained through pyrolysis gas chromatography and mass spectrometry. These distinctions arise due to differences in water solubility, molecular weights, volatility, and other factors between biotic and abiotic samples. For example, living cells exhibit distinct water solubility properties compared to petroleum or coal.
Unlocking Scientific Mysteries and Multidisciplinary Potential
This innovative approach is poised to address scientific mysteries, including the biogenicity of ancient sediments and rocks on Earth. It may provide valuable insights into various fields such as biology, paleontology, and archaeology.
The researchers are now considering how this approach could be used to examine the characteristics of ancient fossil cells, including the presence of nuclei or photosynthetic properties. Moreover, it could be applied to analyze charred remains and differentiate types of wood in archaeological contexts.
This groundbreaking method has the potential to benefit astrobiology missions and enhance our understanding of life’s history on Earth and beyond. It may even be employed in future missions to Mars to investigate the possibility of life on the red planet.
Leading experts in the field have hailed this research as an exciting and innovative avenue for astrobiology and the study of Earth’s early history. It opens up new possibilities for identifying life forms based on molecular complexity, without relying on specific biomolecules, which could be unique to Earth’s life.
In conclusion, this AI-based method offers a promising tool for identifying life both on other planets and in Earth’s distant past, with broad applications and implications for astrobiology and related fields.
Read the original article on: Phys Org
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