文章基本信息

标题：KEPLER ECLIPSING BINARY STARS. III. CLASSIFICATION OF KEPLER ECLIPSING BINARY LIGHT CURVES WITH LOCALLY LINEAR EMBEDDING
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作者：Gal Matijevič ; Andrej Prša ; Jerome A. Orosz 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2012
卷号：143
期号：5
DOI：10.1088/0004-6256/143/5/123
语种：English
出版社：American Institute of Physics
摘要：We present an automated classification of 2165 Kepler eclipsing binary (EB) light curves that accompanied the second Kepler data release. The light curves are classified using locally linear embedding, a general nonlinear dimensionality reduction tool, into morphology types (detached, semi-detached, overcontact, ellipsoidal). The method, related to a more widely used principal component analysis, produces a lower-dimensional representation of the input data while preserving local geometry and, consequently, the similarity between neighboring data points. We use this property to reduce the dimensionality in a series of steps to a one-dimensional manifold and classify light curves with a single parameter that is a measure of "detachedness" of the system. This fully automated classification correlates well with the manual determination of morphology from the data release, and also efficiently highlights any misclassified objects. Once a lower-dimensional projection space is defined, the classification of additional light curves runs in a negligible time and the method can therefore be used as a fully automated classifier in pipeline structures. The classifier forms a tier of the Kepler EB pipeline that pre-processes light curves for the artificial intelligence based parameter estimator.
关键词：binaries: eclipsing;methods: data analysis;methods: numerical