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arXiv 2407.09407astro-ph.GA
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利用Gaia探索银河系疏散星团 I. 对第一千秒差距的考察

Exploring Galactic open clusters with Gaia I. An examination in the first kiloparsec

Jeison Alfonso, Alejandro García-Varela, Katherine Vieira

AI总结:

本研究利用Gaia DR3和HDBSCAN算法,在1 kpc内识别出370个疏散星团的成员星并估计其物理参数,相比DR2多发现约40%恒星,显著提升了成员星表精度。

摘要:

背景。自Gaia星表首次发布以来,我们对银河系有了新的认识。其天体测量和测光信息提高了从中获得的疏散星团物理参数的精度。目标。利用Gaia DR3星表,我们旨在为位于1 kpc内的370个银河系疏散星团寻找包括暗星在内的物理成员星。我们还估计了这些星团的年龄、金属丰度、距离模数和消光。方法。我们在天体测量和测光数据上采用HDBSCAN算法来识别疏散星团的成员。随后,我们利用卡方分布在95%置信水平下的马氏度量剔除异常值,从而精炼样本。此外,我们使用PARSEC等龄线来表征恒星参数。结果。我们获得了370个疏散星团的可靠成员星,平均视差误差为0.16 mas。与之前使用Gaia DR2星表的工作相比,我们在这些星团中多识别了约40%的恒星,其中包括G > 17的暗星作为新成员。在聚类应用之前,我们校正了视差零点偏差,以避免可能影响聚类结果的空间分布拉伸。我们的成员星表包括由HDBSCAN通过天体测量和测光识别的并合星。我们注意到,在测光参与聚类的情况下,在较暗端可比仅基于天体测量的聚类多恢复多达10%的恒星,这与通过质量截断筛选恒星相结合,显著减少了具有巨大视差误差的恒星数量。聚类后,我们根据成员星表的测光估计了年龄、Z和AV。

英文摘要:

Context. Since the first publication of the Gaia catalogue a new view of our Galaxy has arrived. Its astrometric and photometric information has improved the precision of the physical parameters of open star clusters obtained from them. Aims. Using the Gaia DR3 catalogue, we aim to find physical stellar members including faint stars for 370 Galactic open clusters located within 1 kpc. We also estimate the age, metallicity, distance modulus and extinction of these clusters. Methods. We employ the HDBSCAN algorithm on both astrometric and photometric data to identify members in the open clusters. Subsequently, we refine the samples by eliminating outliers through the application of the Mahalanobis metric utilizing the chi-square distribution at a confidence level of 95%. Furthermore, we characterize the stellar parameters with the PARSEC isochrones. Results. We obtain reliable star members for 370 open clusters with an average parallax error of 0.16 mas. We identify about 40% more stars in these clusters compared to previous work using the Gaia DR2 catalogue, including faint stars as new members with G > 17. Before the clustering application we correct the parallax zero-point bias to avoid spatial distribution stretching that may affect clustering results. Our membership lists include merging stars identified by HDBSCAN with astrometry and photometry. We note that the use of photometry in clustering can recover up to 10% more stars in the fainter limit than clustering based on astrometry only, this combined with the selection of stars filtering them out by quality cuts significantly reduces the number of stars with huge parallax error. After clustering, we estimate age, Z, and AV from the photometry of the membership lists.

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