变星的结构特性和分类：通过无监督机器学习技术的研究
Structural properties and classification of variable stars: A study through unsupervised machine learning techniques
摘要：
数据科学领域的进步，特别是机器学习领域的进步，以及光学引力透镜实验（OGLE）等变星项目的庞大数据库，鼓励研究人员自动分析和分类不同变星的光变曲线。在本工作中，我们已经证明了主成分分析（PCA）和独立成分分析（ICA）应用于OGLE变星光变曲线的巨大数据库的相对性能，在获得每个光变曲线的相位0到1之间的1000个星等，步长为0.001，以识别基模（FU）和第一泛音（FO）的共振造父变星和大麦云云（LMC）、小麦哲伦云（SMC）以及银河系（MW）的变星分类。我们已经看到，ICA的性能更好地找到Cepheid变量的共振，以及准确地分类大数据集的光变曲线比PCA。利用K-均值聚类算法（CA）对LMC、SMC和MW分别绘制了周期-光度图和色-星等图，发现ICA和K-均值CA是一个非常强大的分类工具，也是预测变星光变曲线性质的工具。
英文摘要：
The advancement in the field of data science especially in machine learning along with vast databases of variable star projects like the Optical Gravitational Lensing Experiment (OGLE) encourages researchers to analyse as well as classify light curves of different variable stars automatically with efficiency. In the present work, we have demonstrated the relative performances of principal component analysis (PCA) and independent component analysis (ICA) applying to huge databases of OGLE variable star light curves after obtaining 1000 magnitudes between phase 0 to 1 with step length 0.001 for each light curves in identifying resonances for fundamental mode (FU) and first overtone (FO) Cepheids and in the classification of variable stars for Large Magellanic Cloud (LMC), Small Magellanic Cloud (SMC) as well as Milky Way (MW). We have seen that the performance of ICA is better for finding resonances for Cepheid variables as well as for accurately classifying large data sets of light curves than PCA. Using K-means clustering algorithm (CA) with respect to independent components (ICs), we have plotted period-luminosity diagrams and colour-magnitude diagrams separately for LMC, SMC and MW and found that ICA along with K-means CA is a very robust tool for classification as well as future prediction on the nature of light curves of variable stars.
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