Discovery of more than 300 unknown exoplanets by Artificial Intelligence in Kepler telescope data

Discovery of more than 300 unknown exoplanets by Artificial Intelligence in Kepler telescope data

Discovery of Exoplanets

In data obtained by a now-defunct exoplanet-hunting telescope, a new artificial intelligence programme detected over 300 previously unknown exoplanets.

Hundreds of thousands of stars have been examined by the Kepler Space Telescope, NASA’s first dedicated exoplanet hunter, in the quest for potentially habitable worlds outside our solar system. Even after the telescope’s demise, the catalogue of probable planets it had accumulated continues to provide new findings. Human scientists look for hints of exoplanets in the data. However, a new programme known as ExoMiner can now duplicate that process and search the library more quickly and efficiently.

The telescope, which was shut down in November 2018, was looking for brief reductions in star brightness. As seen by Kepler, this could be due to the planet crossing in front of the star’s disc. However, exoplanets aren’t responsible for all of these dimmings, and scientists have to employ complicated processes to identify false positives from the genuine thing, according to NASA.

ExoMiner is a sort of artificial intelligence programme known as a neural network, which may learn and enhance its abilities when given enough data.

Kepler produced enormous within 10 years of Service:

  • The telescope learnt hundreds and thousands of planet candidates
  • Around 3,000 exoplanets have been confirmed to date

Scientists going over the Kepler data would look at the light curve for each possible exoplanet and compute how much of the star the planet appears to cover. They’d also look at how long the potential planet appears to take to cross the star’s disc.

 

The ExoMiner’s Process

The ExoMiner method follows the same steps but in a more efficient manner. The researchers were able to add a batch of 301 previously unknown exoplanets to the Kepler planet list at once as a result of this.

“You can be sure it’s a planet when ExoMiner says it’s a planet,” says Hamed Valizadegan, ExoMiner project lead and machine learning manager at NASA Ames Research Center. “Because of the biases that come with human categorization, ExoMiner is very accurate and in some ways more dependable than both existing machine classifiers and the human specialists it’s supposed to mirror.”

Scientists are hoping to use ExoMiner to help sift through data from other existing and forthcoming exoplanet-searching missions now that it has proven its worth. The current Transiting Exoplanet Survey Satellite (TESS) from NASA and the European Space Agency’s Planetary Transits and Oscillations of Stars (PLATO) mission, which will launch in 2026, are two examples.

Unfortunately, none of the newly discovered exoplanets is likely prospects for hosting life because they are outside of their parent stars’ habitable zones.

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