Fifty new planets confirmed in machine learning first

Date:
August 25, 2020
Source:
University of Warwick
Summary:
Fifty potential planets have had their existence confirmed by a
new machine learning algorithm.



FULL STORY ========================================================================== Fifty potential planets have had their existence confirmed by a new
machine learning algorithm developed by University of Warwick scientists.


==========================================================================
For the first time, astronomers have used a process based on machine
learning, a form of artificial intelligence, to analyse a sample of
potential planets and determine which ones are real and which are 'fakes',
or false positives, calculating the probability of each candidate to be
a true planet.

Their results are 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. Their conclusions make the case for using multiple validation techniques,
including their machine learning algorithm, when statistically confirming future exoplanet discoveries.

Many exoplanet surveys search through huge amounts of data from telescopes
for the signs of planets passing between the telescope and their star,
known as transiting. This results in a telltale dip in light from
the star that the telescope detects, but it could also be caused by a
binary star system, interference from an object in the background, or
even slight errors in the camera. These false positives can be sifted
out in a planetary validation process.

Researchers from Warwick's Departments of Physics and Computer Science,
as well as The Alan Turing Institute, built a machine learning based
algorithm that can separate out real planets from fake ones in the large samples of thousands of candidates found by telescope missions such as
NASA's Kepler and TESS.

It was trained to recognise real planets using two large samples of
confirmed planets and false positives from the now retired Kepler
mission. The researchers 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. 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.



========================================================================== Those fifty planets range from worlds as large as Neptune to smaller
than 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.

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." Dr Theo Damoulas
from the University of Warwick Department of Computer Science, and Deputy Director, Data Centric Engineering and Turing Fellow at The Alan Turing Institute, said: "Probabilistic approaches to statistical machine learning
are especially suited for an exciting problem like this in astrophysics
that requires incorporation of prior knowledge -- from experts like Dr Armstrong -- and quantification of uncertainty in predictions. 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.

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."

========================================================================== Story Source: Materials provided by University_of_Warwick. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. David J Armstrong, Jevgenij Gamper, Theodoros Damoulas. Exoplanet
Validation with Machine Learning: 50 new validated Kepler planets.

Monthly Notices of the Royal Astronomical Society, 2020; DOI:
10.1093/ mnras/staa2498 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200825110610.htm

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