Identifying Exoplanets with Machine Learning Methods: A Preliminary Study

The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a considerable investment of manpower, time, and money, but also are limited by the performance of astronomical telescopes.

In this study, we proposed the idea of using machine learning methods to identify exoplanets. We used the Kepler dataset collected by NASA from the Kepler Space Observatory to conduct supervised learning, which predicts the existence of exoplanet candidates as a three-categorical classification task, using decision tree, random forest, naïve Bayes, and neural network; we used another NASA dataset consisted of the confirmed exoplanets data to conduct unsupervised learning, which divides the confirmed exoplanets into different clusters, using k-means clustering.

As a result, our models achieved accuracies of 99.06%, 92.11%, 88.50%, and 99.79%, respectively, in the supervised learning task and successfully obtained reasonable clusters in the unsupervised learning task.

Yucheng Jin, Lanyi Yang, Chia-En Chiang

Comments: 12 pages with 9 figures and 2 tables
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Machine Learning (cs.LG)
Cite as: arXiv:2204.00721 [astro-ph.EP] (or arXiv:2204.00721v1 [astro-ph.EP] for this version)
Submission history
From: Yucheng Jin
[v1] Fri, 1 Apr 2022 23:48:26 UTC (1,071 KB)
https://arxiv.org/abs/2204.00721
Astrobiology,