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arXiv 2303.07752astro-ph.GAastro-ph.SR
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Serpens 中增强的年轻恒星天体族群

The enhanced YSO population in Serpens

Priya Hasan, Mudasir Raja, Md Saifuddin, S N Hasan

AI总结:

本文利用 Gaia DR3 数据,通过交叉匹配和三种密度聚类算法,在 Serpens 区域识别出 822 个年轻恒星天体,并研究其运动学与分布。

摘要:

Serpens 分子云是距离约 300 秒差距处最活跃的恒星形成区域之一,因此非常适合研究年轻的低质量恒星和亚恒星天体。在本文中,针对 Serpens 恒星形成区域,我们从 Gaia DR3 数据中找出年轻恒星天体(YSO)族群的潜在成员,并研究它们的运动学和分布。我们从可用的目录(从 X 射线到红外)中编制了一份包含 656 个 YSO 的星表。我们以此作为参考集,交叉匹配找到 87 颗 Gaia DR3 成员星,以生成具有修正参数的对照样本。我们用这些参数查询 DR3 星表,找到 1196 颗恒星。然后,我们对这个样本应用了三种基于密度的机器学习算法(DBSCAN、OPTICS 和 HDBSCAN),并找到了潜在的 YSO。这三种聚类算法在该区域识别出一个包含 822 个 YSO 成员的共同集合。我们还使用 2MASS 和 WISE 数据对这些天体进行了分类,以研究它们在 Serpens 中的分布和恒星形成的进展。

英文摘要:

The Serpens Molecular Cloud is one of the most active sites of ongoing star formation at a distance of about 300 pc, and hence is very well-suited for studies of young low-mass stars and sub-stellar objects. In this paper, for the Serpens star forming region, we find potential members of the Young Stellar Objects population from the Gaia DR3 data and study their kinematics and distribution. We compile a catalog of 656 YSOs from available catalogs ranging from X-ray to the infrared. We use this as a reference set and cross-match it to find 87 Gaia DR3 member stars to produce a control sample with revised parameters. We queried the DR3 catalog with these parameters and found 1196 stars. We then applied three different density-based machine learning algorithms (DBSCAN, OPTICS and HDBSCAN) to this sample and found potential YSOs. The three clustering algorithms identified a common set of 822 YSO members from Gaia DR3 in this region. We also classified these objects using 2MASS and WISE data to study their distribution and the progress of star formation in Serpens.

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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