We present new fully-automatic classification model to select extragalactic objects within astronomy photometric catalogs. Construction of the our classification model is based on the three important procedures: 1) data representation to create feature space; 2) building hypersurface in feature space to limit range of features (outliers detection); 3) building hyperplane separating extragalactic objects from the galactic ones. We trained our model with 1.7 million objects (1.4 million galaxies and quasars, 0.3 million stars). The application of the model is presented as a photometric catalog of 38 million extragalactic objects, identified in the WISE and Pan-STARRS catalogs cross-matched with each other.
翻译:我们提出了在天文学光度测量目录中选择星系外物体的新的全自动分类模型,我们的分类模型是根据三个重要程序建造的:1)数据表示以创建地物空间;2)在地物空间建造超表层以限制地物范围(外星探测);3)建造高空飞机,将银河系外物体与银河系外物体分离;我们用170万个物体(140万个星系和类星,30万颗恒)对模型进行了培训;该模型的应用作为由WISE和Pan-STARRS目录相互匹配的3 800万个外星体组成的光度目录进行展示。