论文标题
使用机器学习方法识别系外行星：初步研究
Identifying Exoplanets with Machine Learning Methods: A Preliminary Study
论文作者
论文摘要
长期以来，在天文学中，发现可居住的系外行星一直是一个备受激烈的话题。系外行星识别的传统方法包括Wobble方法，直接成像，重力微透镜等，不仅需要大量的人力，时间和金钱投资，而且还受到天文望远镜的性能的限制。在这项研究中，我们提出了使用机器学习方法识别系外行星的想法。我们使用NASA从开普勒太空天文台收集的开普勒数据集进行了监督学习，这将使用决策树，随机森林，幼稚的贝叶斯和神经网络预测候选系外行星候选者的存在为三类分类任务；我们使用了另一个NASA数据集，该数据集由已确认的系外行星数据组成，以进行无监督的学习，该学习使用K-均值聚类将确认的外部行星划分为不同的群集。结果，我们的模型在监督的学习任务中分别达到了99.06％，92.11％，88.50％和99.79％的准确性，并在无处可比的学习任务中成功获得了合理的群集。
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.
