Artificial Intelligence to Predict Probability of Life on other Planets

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The study uses Artificial Neural Networks (ANNs) to classify planets into five types, estimating a probability of life in each case, which could be used in future interstellar exploration missions

Technological advancements in Artificial Intelligence (AI) may help to predict the probability of life on other planets. Composite image showing an infrared view of Saturn’s moon Titan, taken from NASA’s Cassini spacecraft suggest that Titan has the highest habitability rating of any world other than Earth, based on factors such as availability of energy, and various surface, and atmosphere characteristics. Research team from Plymouth University presented the study at the European Week of Astronomy and Space Science (EWASS) in Liverpool on 4 April, 2018 by Mr Christopher Bishop.

Artificial neural networks attempt to replicate functioning of human brain. This tool is used in machine learning and it can identify complex patterns for a biological brain to process. All five of objects predicted by team are rocky bodies known to have atmospheres. They are among the most potentially habitable objects in Solar System. Mr Bishop comments, “We’re currently interested in these ANNs for prioritising exploration for a hypothetical, intelligent, interstellar spacecraft scanning an exoplanet system at range.” He adds, “We’re also looking at the use of large area, deployable, planar Fresnel antennas to get data back to Earth from an interstellar probe at large distances. This would be needed if the technology is used in robotic spacecraft in the future.”

Atmospheric observations of the five Solar System bodies are presented as inputs to the network, which is are classified on planetary type. As life is currently known only to exist on Earth, the classification uses a ‘probability of life’ metric, which is based on the relatively well-understood atmospheric and orbital properties of the five target types. The technique may also be ideally suited to selecting targets for future observations, given the increase in spectral detail expected from upcoming space missions such ESA’s Ariel Space Mission and NASA’s James Webb Space Telescope.

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