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arXiv 1905.03063astro-ph.SRastro-ph.GAastro-ph.IM
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用机器学习方法识别$Gaia$ DR2 x AllWISE星表中的年轻恒星候选天体

Identification of Young Stellar Object candidates in the $Gaia$ DR2 x AllWISE catalogue with machine learning methods

G. Marton, P. Ábrahám, E. Szegedi-Elek, J. Varga, M. Kun, Á. Kóspál, E. Varga-Verebélyi, S. Hodgkin, L. Szabados, R. Beck, Cs. Kiss

摘要:

第二份$Gaia$数据发布(DR 2)包含超过16亿个天体的天体测量和测光数据,平均$Gaia$ $G$星等为$<$ 20.7,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$Gaia$ DR 2数据库与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星形成复合体的三维结构,并表明我们的方法分类的YSO的空间分布与文献中的最新结果一致。(2)我们使用我们的目录对已发布的$Gaia$科学警报进行分类。由于$Gaia$在多个历元测量源,它可以有效地发现瞬态事件,包括YSO的突然亮度变化引起的动力学过程的拱星盘。然而,在许多情况下,发布的警报源的物理性质并不清楚。与我们的新目录交叉检查显示,大约30%以上的已发布的$Gaia$警报很可能归因于YSO活动。该目录还有助于在今后的$Gaia$警报中识别YSO。

英文摘要:

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 database with 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% 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 disk. 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% 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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