NASA Discovers 50 New Planets From Old Data Using Artificial Intelligence-Know More!

Check out how NASA discovered 50 new planets using Artificial Intelligence

Recently, there have been reports of multiple planets been discovered in deep space using telescopes or data from different missions. Chances are that a gigantic series of planets can be confirmed analyzing data from previous missions. David Armstrong at the University of Warwick, United Kingdom, led a research team that was able to form an algorithm (artificial intelligence) charting out efficiently which one is a planet or glitch or an asteroid and so on.

Researchers used NASA’s Transiting Exoplanet Survey Satellite to analyze the data. To simplify it, a fading brightness indicates that something is passing by a star. It doesn’t always have to be a planet but it helps to limit the search. This led to the creation of a machine learning algorithm that was applied to NASA’s Kepler mission to bifurcate planets from other celestial bodies. The algorithm came across as a quantum leap as the AI was able to establish the existence of as many as 50 planets.

“The algorithm we have developed lets us take 50 candidates across the threshold for planet validation, upgrading them to real planets,” Armstrong said in a Warwick release on August 25.

As states, this technology will help scientists lay emphasis on their resources and time on confirmed planets instead of trying to identify new ones with a lengthy procedure till now. This will also help them position planets that can have a similar atmosphere like Earth’s.

“Rather than saying which candidates are more likely to be planets, we can now say what the precise statistical likelihood is. Where there is less than a 1% chance of a candidate being a false positive, it is considered a validated planet,” said Armstrong.

The algorithm is still in its early stages of development despite the breakthrough. However, it will be further used on the extensive pile of data collected from projects like TESS and ESA’s planned PLATO mission.

“We still have to spend time training the algorithm, but once that is done it becomes much easier to apply it to future candidates,” Armstrong said. “You can also incorporate new discoveries to progressively improve it.”

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