Science

Finding Aliens: Scientists to take help of Artificial Intelligence to trace Alien Life on exoplanets

For the study, scientists have harnessed the power of artificial intelligence and artificial neural networks (ANNs) to classify planets into five types, based on the likelihood of supporting extraterrestrial life. Based on these groups, scientists will further conduct interstellar missions to find the alien life.

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Search for alien life has gained pace in recent times and space agencies are working hard to find the proof of extraterrestrial life. Meanwhile, a team of scientists from the Plymouth University believes that we should take help of artificial intelligence to find the alien life. According to researchers, artificial intelligence has witnessed rapid developments and it can help us in predicting the probability of life on other planets.

For the study, scientists have harnessed the power of artificial intelligence and artificial neural networks (ANNs) to classify planets into five types, based on the likelihood of supporting extraterrestrial life. Based on these groups, scientists will further conduct interstellar missions to find the alien life.

Lead study author Mr Christopher Bishop explains that planets in all the five groups have an atmosphere and possibility of life. Some planets are very similar to present day earth and are present in Goldilocks zone while some are more like Mars and Venus.

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.”

As per wiki, Artificial neural networks(ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” (i.e. progressively improve performance on) tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the results to identify cats in other images. They do this without any a prior knowledge about cats, e.g., that they have fur, tails, whiskers and cat-like faces. Instead, they evolve their own set of relevant characteristics from the learning material that they process.

An ANN is based on a collection of connected units or nodes called artificial neurons (a simplified version of biological neurons in an animal brain). Each connection (a simplified version of a synapse) between artificial neurons can transmit a signal from one to another. The artificial neuron that receives the signal can process it and then signal artificial neurons connected to it.

Mr Bishop further revealed that his team has trained the ANN by supplying data of hundreds of planets and different spectral profiles to determine the habitability of a planet.

“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” says Dr Angelo Cangelosi, the supervisor of the project.

Scientists believe that the study will give specific targets for Hubble Space Telescope and James Webb Telescope to examine a particular planet for the presence of alien life.

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