Recently, a team of researchers led by David Armstrong at the University of Warwick created a AI breakthrough. The team trained a machine-learning algorithm to identify planets outside our solar system called “exoplanets.” The new machine learning algorithm designed by astronomers and computer scientists from University of Warwick confirms 50 new potential exoplanets in telescope data
Dr David Armstrong, from the University of Warwick Department of Physics, said
The algorithm we have developed lets us take fifty candidates across the threshold for planet validation, upgrading them to real planets.
We 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, 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.
The algorithm was trained to distinguish between signs of real planets and false positives. The new technique is faster than previous techniques, can be automated, and improved with further training.
The researchers used data sets from NASA’s planet-hunting Kepler mission to demonstrate AI’s great potential. Processing large amounts of data to make statistically valid predictions is something where AI surpasses human capability.
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.
Dr Armstrong adds
Almost 30% 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.
We 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 it.
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.
Those fifty planets 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.