论文标题
使用基于特征的机器学习，将12,000 A和F星的开普勒光曲线分类
Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning
论文作者
论文摘要
随着开普勒和苔丝（Tess）等大规模调查的可用性，对自动化方法的迫切需要根据已知的可变恒星类别对光曲线进行分类。我们引入了一种用于对光曲线进行分类的新算法，该算法比较了7000个时间序列的功能，以找到最有效地对一组光曲线进行分类的算法。我们将方法应用于有效温度在6500--10,000k范围内的恒星的开普勒光曲线。我们表明，样品可以在可解释的五维特征空间中有意义地表示，该空间将七个主要的光曲线分开（Delta Scuti Stars，Gamma Doradus Stars，RR Lyrae Star，RR Lyrae Star，旋转变量，接触要，与二进制的二进制文件，分离的Eclipsed Eclips eclips Binaries和Non-Non-Variables）。我们使用高斯混合物模型分类器在独立的开普勒恒星测试集上达到了平衡的分类精度。我们使用我们的方法对第9季度的12,000个开普勒光曲线进行分类，并提供结果的目录。我们进一步概述了基于搜索目录的概率密度的信心启发式，并提取正确分类的变量星的候选列表。
With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light curves that compares 7000 time-series features to find those which most effectively classify a given set of light curves. We apply our method to Kepler light curves for stars with effective temperatures in the range 6500--10,000K. We show that the sample can be meaningfully represented in an interpretable five-dimensional feature space that separates seven major classes of light curves (delta Scuti stars, gamma Doradus stars, RR Lyrae stars, rotational variables, contact eclipsing binaries, detached eclipsing binaries, and non-variables). We achieve a balanced classification accuracy of 82% on an independent test set of Kepler stars using a Gaussian mixture model classifier. We use our method to classify 12,000 Kepler light curves from Quarter 9 and provide a catalogue of the results. We further outline a confidence heuristic based on probability density with which to search our catalogue, and extract candidate lists of correctly-classified variable stars.
