Artificial intelligence may be key to finding life on Mars
An experiment on a Martian analogue in northern Chile has tested the usefulness of teaming planetary robots with artificial intelligence to focus the search for life in the most effective way.
In an article published in Nature Astronomyan interdisciplinary study led by Kim Warren-Rhodes, Principal Investigator at the SETI Institute, she mapped the rare life hidden in domes of salt, rock, and crystals in the Salar de Pajonales, on the border between the Chilean Atacama Desert and the Altiplano.
Next, Warren-Rhodes worked with co-investigators Michael Phillips (Johns Hopkins Applied Physics Laboratory) and Freddie Kalaitzis (Oxford University) to train a machine learning model to recognize the patterns and rules associated with its distributions, so that it could learn to predict and find those same distributions in data that it had not been trained on.
In this case, by combining statistical ecology with artificial intelligence/machine learning, scientists were able to locate and detect biofirmas up to 87.5% of the time (compared to 10% with random search) and decrease the area needed for the search by up to 97%.
"Our framework allows us to combine the power of statistical ecology with machine learning to discover and predict the patterns and rules by which nature survives and distributes itself in the harshest landscapes on Earth," he says. Rhodes it's a statement.
"We hope that other astrobiology teams will adapt our approach to mapping other habitable environments and biosignals. With these models, we can design roadmaps and bespoke algorithms to guide rovers to places most likely to harbor past or present life, however hidden or rare."
Ultimately, similar algorithms and machine learning models for many different types of ehabitable environments and biosignatures pthey could be automated aboard planetary robots to effectively guide mission planners to areas of any scale most likely to contain life.
Rhodes and the team NASA Astrobiology Institute (NAI) of the SETI Institute used the Salar de Pajonales as an analogue of Mars. Pajonales is a dry, hyperarid, high altitude (3,541 m) and high U/V saline bed, considered inhospitable to many forms of life, but still habitable.
During the NAI project field campaigns, the team collected more than 7,765 images and 1,154 samples and tested instruments to detect photosynthetic microbes living inside salt domes, rocks and alabaster crystals. These microbes exude pigments that represent a possible biosignature in the NASA Life Detection Ladder.
In pajonales, drone flight imagery connected simulated orbital data (HiRISE) with ground surveys and 3D topographic mapping to extract spatial patterns. The results of the study confirm (statistically) that microbial life in the Pajonales terrestrial analogue site is not randomly distributed, but is concentrated in irregular biological points strongly linked to water availability at km to cm scales.
Next, the team trained convolutional neural networks (CNNs) to recognize and predict macroscale geological features on Pajonales - some of which, such as patterned soil or polygonal networks, are also found on Mars - and substrates at microscale (or "microhabitats") most likely to contain biosignatures.
Like the Perseverance team at Marte, the researchers tested how to effectively integrate a UAV/drone with rovers, drills and ground instruments (for example, VISIR on 'MastCam-Z' and Raman on 'SuperCam' on the Mars 2020 Perseverance rover).