50 New Exoplanets discovered by Kelper telescope

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Exoplanets
Exoplanets

50 New Exoplanets discovered by Kelper telescope. During the last decades we have made great strides in the search for planets outside the Solar System. For them, space telescopes like Kepler have been in charge of collecting a gigantic amount of data. Now, analyzing all that data is a tedious task for astronomers. What if an AI took care of them?

This is what researchers at the University of Warwick in collaboration with the Alan Turning Institute thought . So they trained an artificial intelligence to learn how to search the telescope data samples and thus find new exoplanets not yet identified. The result? 50 new exoplanets at once.

During the last two decades, more than 4,200 exoplanets have been discovered throughout the Universe. This is important because, by analogy, if they are similar to Earth when orbiting a star, they have a better chance of harboring life or being habitable. To discover these exoplanets , a technique called the transit method is generally used. The method is based on analyzing the amount of light coming from a star and how that amount temporarily decreases when an exoplanet comes between the star and the telescope, in other words, when it transits.

Kepler space telescope
Kepler space telescope

With this technique, astronomers can know that “there is something there”, but it is not a direct confirmation that it is an exoplanet orbiting that star. It can be a binary star system, it can be an asteroid close to the telescope that it has crossed, it can be a telescope error … That is why astronomers have to analyze all possible candidates one by one to see if they really do. It’s about an exoplanet or a false positive.

Discarding false positives

And this is where the University of Warwick’s machine learning system has come into play. He was trained with two large data samples of exoplanets discovered by the Kepler space telescope . One of those samples was already analyzed by astronomers and the exoplanets were confirmed, the other were known false positives. The AI ​​was tasked with comparing the two samples and learning what subtle differences there are between a real exoplanet and a false positive.

After being trained, the artificial intelligence analyzed a third sample with possible exoplanets that had not yet been analyzed. In analyzing the probabilities that they were false positives or not, the AI ​​selected only those that were less than 1% false positives and classified them as exoplanets. With this he was able to discover a total of 50 new exoplanets at once.

Exoplanets
Exoplanets

One of the most important uses of machine learning algorithms is to analyze large samples of data in a much faster and more automated way. Essentially what this AI has done by observing the vast Universe filled with stars and possible exoplanets.

Find More On: Warwick University

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