Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods
Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods
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DOI：
10.1093/mnras/stz1301
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发表时间：
2019-05
影响因子：
4.8
通讯作者：
G. Marton;P. Ábrahám;E. Szegedi-Elek;J. Varga;M. Kun;Á. Kóspál;Á. Kóspál;E. Varga-Vereb'elyi-E.-Varga-Vereb'ely
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文献类型：
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作者：
G. Marton;P. Ábrahám;E. Szegedi-Elek;J. Varga;M. Kun;Á. Kóspál;Á. Kóspál;E. Varga-Vereb'elyi-E.-Varga-Vereb'ely
第二次盖亚数据发布（DR2）包含超过16亿个平均盖亚G星等小于20.7的天体的天体测量和光度数据，包括许多处于不同演化阶段的年轻恒星天体。为了探索银河系的YSO人口，我们将Gaia DR2数据库与宽视场红外巡天探测器（WISE）和普朗克测量相结合，并使用机器学习技术（如支持向量机，随机森林或神经网络）制作了YSO的全天概率目录。我们的输入目录包含来自DR2xAllWISE交叉匹配表的1.03亿个对象。我们将每个物体分为四个主要类别：YSO，河外天体，主序星和演化恒星。在90%的概率阈值下，我们确定了1 129 295名YSO候选人。为了证明我们的YSO星表的质量和潜力，我们在这里介绍了它的两个应用：（1）我们探索了猎户座A恒星形成复合体的3D结构，并表明我们的方法分类的YSO的空间分布与最近的文献结果一致。(2)我们使用我们的目录来分类已发布的盖亚科学警报。由于盖亚在多个时期测量源，它可以有效地发现瞬态事件，包括由其拱星盘的动态过程引起的YSO的突然亮度变化。然而，在许多情况下，发布的警报源的物理性质是未知的。与我们新目录的交叉检查显示，大约30%以上的已发布盖亚警报很可能归因于YSO活动。该目录还有助于在未来的盖亚警报中识别YSO。
The second Gaia Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean Gaia G magnitude <20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the Gaia DR2 data base with Wide-field Infrared Survey Explorer (WISE) and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match table. We classified each object into four main classes: YSOs, extragalactic objects, main-sequence stars, and evolved stars. At a 90 per cent probability threshold, we identified 1 129 295 YSO candidates. To demonstrate the quality and potential of our YSO catalogue, here we present two applications of it. (1) We explore the 3D structure of the Orion A star-forming complex and show that the spatial distribution of the YSOs classified by our procedure is in agreement with recent results from the literature. (2) We use our catalogue to classify published Gaia Science Alerts. As Gaia measures the sources at multiple epochs, it can efficiently discover transient events, including sudden brightness changes of YSOs caused by dynamic processes of their circumstellar disc. However, in many cases the physical nature of the published alert sources are not known. A cross-check with our new catalogue shows that about 30 per cent more of the published Gaia alerts can most likely be attributed to YSO activity. The catalogue can be also useful to identify YSOs among future Gaia alerts.