Discovery Of 50 New Planets From Old NASA Data

In a groundbreaking first, British researchers have identified 50 new planets using artificial intelligence.

A research team led by David Armstrong at the University of Warwick in the United Kingdom created a machine learning algorithm and trained it using data on confirmed planets and false-positives from NASA’s retired Kepler mission.

The scientists then used the algorithm on a dataset of still unconfirmed planetary candidates from Kepler, resulting in fifty new confirmed planets and the first to be validated by machine learning.

Their results were reported in a new study published in the Monthly Notices of the Royal Astronomical Society, where they also perform the first large scale comparison of such planet validation techniques.

Previous machine learning techniques have ranked candidates, but never determined the probability that a candidate was a true planet by themselves, a required step for planet validation.

Discovery of 50 new planets: 

These 50 exoplanets range from worlds as large as Neptune to smaller than the Earth, with orbits as long as 200 days to as little as a single day. By confirming that these fifty planets are real, astronomers can now prioritise these for further observations with dedicated telescopes.

The algorithm the researchers have developed lets them take fifty candidates across the threshold for planet validation, upgrading them to real planets. They hope to apply this technique to large samples of candidates from current and future missions like TESS and PLATO.

In terms of planet validation, no-one has used a machine learning technique before. Machine learning has been used for ranking planetary candidates but never in a probabilistic framework, which is what you need to truly validate a planet. Rather than saying which candidates are more likely to be planets, they can now say what the precise statistical likelihood is. Where there is less than a 1 per cent chance of a candidate being a false positive, it is considered a validated planet.

The algorithm used by the NASA Scientist:

Probabilistic approaches to statistical machine learning are especially suited for an exciting problem like this in astrophysics that requires the incorporation of prior knowledge. A prime example when the additional computational complexity of probabilistic methods pays off significantly.

Once built and trained the algorithm is faster than existing techniques and can be completely automated, making it ideal for analysing the potentially thousands of planetary candidates observed in current surveys like TESS.

The researchers argue that it should be one of the tools to be collectively used to validate planets in future.

Almost 30 per cent of the known planets to date have been validated using just one method, and that`s not ideal. Developing new methods for validation is desirable for that reason alone. But machine learning also lets us do it very quickly and prioritise candidates much faster. The researchers still have to spend time training the algorithm, but once that is done it becomes much easier to apply it to future candidates.

You can also incorporate new discoveries to progressively improve them. A survey like TESS is predicted to have tens of thousands of planetary candidates and it is ideal to be able to analyse them all consistently. Fast, automated systems like this that can take us all the way to validated planets in fewer steps let us do that efficiently.

Image Courtesy: www.unilad.co.uk

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