Astronomers can predict life on other planets with the help of Artificial Intelligence (AI). It is said that using artificial neural networks (ANNs), researchers from Britain's Plymouth University classified planets into five types, based on whether they are most like the present-day Earth, the early Earth, Mars, Venus or Saturn's moon Titan, estimating a probability of life in each case.
These five objects are rocky bodies also known to have atmospheres and are among the most potentially habitable objects in the Solar System.
"We're currently interested in these ANNs for prioritising exploration for a hypothetical, intelligent, interstellar spacecraft scanning an exoplanet system at range," said Christopher Bishop from the varsity.
"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," Bishop added. ANNs are systems that attempt to replicate the way the human brain learns.
When asked to classify the Atmospheric observations -- known as spectra -- of the five Solar System bodies are presented as inputs to the network in terms of the planetary type.
Currently, life exist only 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. "Given the results so far, this method may prove to be extremely useful for categorising different types of exoplanets using results from ground-based and near-Earth observatories," said Angelo Cangelosi, the supervisor of the project.
The work was presented at the European Week of Astronomy and Space Science (EWASS) in Liverpool.
With Inputs from Agencies