Machine-learning identification of extragalactic objects in the optical-infrared all-sky surveys

被引：0

作者：

Khramtsov, Vladislav ^{[1
]}

Akhmetov, Vladimir ^{[2
]}

机构：

[1] Kharkov Natl Univ, Dept Astron & Space Informat, Kharkov, Ukraine

[2] Kharkov Natl Univ, Inst Astron, Lab Astrometry, Kharkov, Ukraine

来源：

2018 IEEE 13TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT), VOL 1 | 2018年

基金：

美国国家科学基金会; 美国国家航空航天局; 美国安德鲁·梅隆基金会;

关键词：

classification; data mining; machine learning; neural networks; support vector machines;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We present new fully-automatic classification model to select extragalactic objects within astronomical 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.

引用

页码：72 / 75

页数：4

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