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Artificial Intelligence can help us to predict probability of life on other planets

Monday April 9, 2018 5:41 PM, Numair M Hesham,

AI Predicts Life

Washington: Artificial Intelligence (AI) can help us to predict the probability of life on other planets, according to a new study by Plymouth University.

The study uses Artificial Neural Networks (ANNs) to classify planets into five different types, based on whether they are most like the present-day Earth, the early Earth, Mars, Venus or Saturn's moon Titan. All five of these objects are rocky bodies known to have atmospheres, and are among the most potentially habitable objects in our Solar System.

Artificial Neural Networks are systems that attempt to replicate the way human brain learns. They are one of the main tools used in machine learning, and are particularly good at identifying patterns that are too complex for a biological brain to process.

The study is presented at the European Week of Astronomy and Space Science (EWASS) in Liverpool last week by Christopher Bishop.

"We're currently interested in these ANNs for prioritizing exploration for a hypothetical, intelligent, interstellar spacecraft scanning an exoplanet system at range”, Christopher Bishop, who presented the study at the European Week of Astronomy and Space Science (EWASS) in Liverpool last week, said.

"We are also looking at using of larger area, deployable, planar Fresnel antennas to get data back to Earth from an interstellar probe at longer distance. This would be needed if the technology is used in robotic spacecraft in the future”, he said.

Bishop has trained the network with over a hundred different spectral profiles, each with several hundred parameters that contribute to habitability. So far, the network performs well when presented with a test spectral profile that it hasn't seen before.

“Given the results so far, this method may prove to be extremely useful for categorizing different types of exoplanets using results from ground-based and near Earth observatories" says Dr. Angelo Cangelosi, the supervisor of the project.

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, according to Royal Astronomical Society.

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