论文标题
使用Gaia DR2的45个开放群集的基本参数,改进的灭绝校正和金属梯度先验
Fundamental parameters for 45 open clusters with Gaia DR2, an improved extinction correction and a metallicity gradient prior
论文作者
论文摘要
距离,年龄和灭绝等开放群集的可靠基本参数是我们对银河结构和恒星进化的理解的关键。在这项工作中,我们使用{\ it gaia} dr2调查\ emph {新的光学可见开放式群集和候选者的新目录中列出的45个开放群集}(daml),但没有以前基于{\ ith iTage gaia} dr2的Astrestric成员估算。在选择这项研究的目标的过程中,我们发现一些基于{\ it gaia} dr2的最近论文中报告为新发现的簇已经在Daml中列出了。使用应用于{\ it Gaia} DR2天文测量的最大似然方法确定群集成员资格。这使我们能够估算所有研究簇的平均正确运动和平均视差。还确定了12个簇的平均径向速度,其中7个没有以前发表的值。我们已经改进了我们的等距拟合代码,以使用对{\ it gaia} dr2 dr2光度频段通路和银河系丰度梯度作为金属性的{\ it gaia} dr2光度阶段来解决星际灭绝。更新的过程通过具有高质量$ [Fe/h] $确定的集群样本进行了验证。然后,我们对文献进行了批判性审查,并验证了我们的群集参数确定代表了对先前值的实质性改善。
Reliable fundamental parameters of open clusters such as distance, age and extinction are key to our understanding of Galactic structure and stellar evolution. In this work we use {\it Gaia} DR2 to investigate 45 open clusters listed in the \emph{New catalogue of optically visible open clusters and candidates} (DAML) but with no previous astrometric membership estimation based on {\it Gaia} DR2. In the process of selecting targets for this study we found that some clusters reported as new discoveries in recent papers based on {\it Gaia} DR2 were already known clusters listed in DAML. Cluster memberships were determined using a maximum likelihood method applied to {\it Gaia} DR2 astrometry. This has allowed us to estimate mean proper motions and mean parallaxes for all investigated clusters. Mean radial velocities were also determined for 12 clusters, 7 of which had no previous published values. We have improved our isochrone fitting code to account for interstellar extinction using an updated extinction polynomial for the {\it Gaia} DR2 photometric band-passes and the Galactic abundance gradient as a prior for metallicity. The updated procedure was validated with a sample of clusters with high quality $[Fe/H]$ determinations. We then did a critical review of the literature and verified that our cluster parameter determinations represent a substantial improvement over previous values.