论文标题
可变星的结构特性和分类：通过无监督的机器学习技术进行的研究
Structural properties and classification of variable stars: A study through unsupervised machine learning techniques
论文作者
论文摘要
数据科学领域的进步，尤其是在机器学习中，以及诸如光学重力镜头实验（OGLE）（OGLE）（OGLE）等可变恒星项目的庞大数据库，鼓励研究人员以效率自动分析不同可变星的光曲线。在目前的工作中，我们已经证明了主要成分分析（PCA）和独立组件分析（ICA）的相对性能（ICA），该表现适用于OGLE可变的巨大恒星光曲线数据库，该数据库在0阶段0到1之间获得了1000个幅度，而步长为0.001，每个光曲线均具有0.001的步长0.001，以确定基本模式（FU）和First of Firstantose（FU）（FO）和FO clily（FO）和cliele（FO）的均值（FO）和cline clill and fo light offose（FU）和c。 （LMC），小麦芽云（SMC）以及银河系（MW）。我们已经看到，ICA的性能比PCA相比，ICA的性能更擅长于查找CepheID变量的共振以及准确地对大数据集进行分类。 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.
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.
