We present the construction of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm. We focus on the targeted identification of eclipsing binaries that demonstrate a feature known as the O’Connell effect. Our proposed methodology maps stellar variable observations to a new representation known as distribution fields (DFs). Given this novel representation, we develop a metric learning technique directly on the DF space that is capable of specifically identifying our stars of interest. The metric is tuned on a set of labeled eclipsing binary data from the Kepler survey, targeting particular systems exhibiting the O’Connell effect. The result is a conservative selection of 124 potential targets of interest out of the Villanova Eclipsing Binary Catalog. Our framework demonstrates favorable performance on Kepler eclipsing binary data, taking a crucial step in preparing the way for large-scale data volumes from next-generation telescopes such as LSST and SKA.

中文翻译：

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专为O'Connell效应遮蔽二进制文件设计的检测指标

我们介绍了一种新颖的时域签名提取方法的构建和支持的监督模式检测算法的开发。我们着重于蚀性二进制文件的目标识别，该二进制文件演示了称为O'Connell效应的功能。我们提出的方法将恒星变量观测值映射到称为分布域（DF）的新表示形式。有了这种新颖的表示方式，我们就可以直接在DF空间上开发一种度量学习技术，该技术可以专门识别我们感兴趣的恒星。该度量标准是根据开普勒调查中一组标记的日蚀二进制数据进行调整的，这些数据针对展现O'Connell效应的特定系统。结果是从Villanova Eclipsing Binary Catalog中保守选择了124个潜在目标。