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首页> 外文期刊>Astronomy and astrophysics >Kernel spectral clustering of time series in the CoRoT exoplanet database
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Kernel spectral clustering of time series in the CoRoT exoplanet database

机译：CoRoT系外行星数据库中时间序列的核频谱聚类

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Context. Detection of contaminated light curves and irregular variables has become a challenge when studying variable stars in large photometric surveys such as that produced by the CoRoT mission. Aims. Our goal is to characterize and cluster the light curves of the first four runs of CoRoT, in order to find the stars that cannot be classified because of either contamination or exceptional or non-periodic behavior. Methods. We study three different approaches to characterize the light curves, namely Fourier parameters, autocorrelation functions (ACF), and hidden Markov models (HMMs). Once the light curves have been transformed into a different input space, they are clustered, using kernel spectral clustering. This is an unsupervised technique based on weighted kernel principal component analysis (PCA) and least squares support vector machine (LS-SVM) formulations. The results are evaluated using the silhouette value. Results. The most accurate characterization of the light curves is obtained by means of HMM. This approach leads to the identification of highly contaminated light curves. After kernel spectral clustering has been implemented onto this new characterization, it is possible to separate the highly contaminated light curves from the rest of the variables. We improve the classification of binary systems and identify some clusters that contain irregular variables. A comparison with supervised classification methods is also presented.
机译：上下文。在由CoRoT任务产生的大型光度学调查中研究变星时，检测受污染的光曲线和不规则变量已成为一项挑战。目的我们的目标是表征CoRoT的前四次运行的光曲线并对其进行聚类，以找到由于污染，异常或非周期性行为而无法归类的恒星。方法。我们研究了三种表征光曲线的方法，即傅立叶参数，自相关函数（ACF）和隐马尔可夫模型（HMM）。一旦将光曲线转换为不同的输入空间，就可以使用内核光谱聚类对它们进行聚类。这是基于加权核主成分分析（PCA）和最小二乘支持向量机（LS-SVM）公式的无监督技术。使用轮廓值评估结果。结果。光曲线的最准确表征是通过HMM获得的。这种方法可以识别出高度污染的光曲线。在将内核光谱聚类应用于此新特性后，可以将高度污染的光曲线与其余变量分开。我们改进了二元系统的分类，并确定了一些包含不规则变量的聚类。还提出了与监督分类方法的比较。

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