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Discriminating Variable Star Candidates in Large Image Databases from the HiTS Survey Using NMF

机译：使用NMF从HITS调查中区分大型图像数据库中的变量星候选者

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New instruments and technologies are allowing the acquisition of large amounts of data from astronomical surveys. Nowadays there is a pressing need for autonomous methods to discriminate the interesting astronomical objects in the vast sky. The High Cadence Transient Survey (HiTS) project is an astronomical survey that is trying to find a rare transient event that occurs during the first instants of a supernova. In this paper we propose an autonomous method to discriminate stellar variability from the HiTS database, that uses a feature extraction scheme based on Non-negative matrix factorization (NMF). Using NMF, dictionaries of image prototypes that represent the data in a compact way are obtained. The projections of the dataset into these dictionaries are fed into a random forest classifier. NMF is compared with other feature extraction schemes, on a subset of 500,000 transient candidates from the HiTS survey. With NMF a better class separability at feature level is obtained which enhances the classification accuracy significantly. Using the NMF features less than 4% of the true stellar transients are lost, at a manageable false positive rate of 0.1%.
机译：新仪器和技术允许收购来自天文调查的大量数据。如今，需要一种紧迫的方法，以区分巨大的天空中有趣的天文物体。高Cadence瞬态调查（命中）项目是一个天文调查，该调查试图找到一个罕见的瞬态事件，即在超新星的第一阶段。在本文中，我们提出了一种自主方法，以区分从HITS数据库中的恒星可变性，它使用基于非负矩阵分解（NMF）的特征提取方案。使用NMF，获得以紧凑的方式表示数据的图像原型的字典。数据集将数据集的投影送入随机林分类器。将NMF与其他特征提取方案进行比较，从HITS调查中的500,000个瞬态候选者的子集中进行比较。利用NMF，获得了特征级别的更好的阶级可分离性，从而显着提高了分类精度。使用小于4％的真正恒星瞬态的NMF功能丢失，可管理的假阳性率为0.1％。

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