Machine learning classification of Gaia Data Release 2

被引：18

作者：

Bai, Yu ^{[1
]}

Liu, Ji-Feng ^{[1
,2
]}

Wang, Song ^{[1
]}

机构：

[1] Chinese Acad Sci, Natl Astron Observ, Key Lab Opt Astron, Beijing 100101, Peoples R China

[2] Univ Chinese Acad Sci, Coll Astron & Space Sci, Beijing 100049, Peoples R China

来源：

RESEARCH IN ASTRONOMY AND ASTROPHYSICS | 2018年 / 18卷 / 10期

基金：

中国国家自然科学基金;

关键词：

methods: data analysis; stars: general; Gaia catalog; ROBUST MORPHOLOGICAL CLASSIFICATION; SUPPORT VECTOR MACHINES; FIELD;

D O I：

10.1088/1674-4527/18/10/118

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculate suggestions for large amounts of data. We apply machine learning classification to 85 613 922 objects in the Gaia Data Release 2, based on a combination of Pan-STARRS 1 and AllWISE data. The classification results are cross-matched with the Simbad database, and the total accuracy is 91.9%. Our sample is dominated by stars, similar to 98%, and galaxies make up 2%. For the objects with negative parallaxes, about 2.5% are galaxies and QSOs, while about 99.9% are stars if the relative parallax uncertainties are smaller than 0.2. Our result implies that using the threshold of 0 < sigma(pi)/pi < 0.2 could yield a very clean stellar sample.

引用

页数：4

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