标题： 基于极端去卷积高斯混合模型的盖亚DR2中431个开放星团的成员星列表 显示英文标题

标题： Membership lists for 431 open clusters in Gaia DR2 using extreme deconvolution gaussian mixture models

摘要： 开放星团是由同时形成的一组恒星组成，使它们成为测试银河系盘中恒星形成、恒星演化和动力学理论的理想实验室。 然而，开放星团的效用可能受到其已知成员的准确性和完整性的限制。 在这里，我们采用一种“自上而下”的技术，{\it 极端反卷积高斯混合模型}（XDGMM），通过拟合视线方向上的恒星视差和自行分布来提取和评估来自盖亚DR2的已知开放星团。 极端去卷积技术可以恢复天体测量量的固有分布，考虑误差的完整协方差矩阵；这使得即使在面对相对不确定的测量数据时，也能识别开放星团成员。 到目前为止，开放星团的研究只将极端去卷积应用于特定系统的个别搜索。 我们使用XDGMM来描述Ahumada等人2007年报告的开放星团，并能够恢复其中的420个星团（98.1%）。 我们的成员列表包含了之前已知星团成员的绝大多数（$>95\%$）。 我们还识别出一个新的、显著的且相对较暗的星团成员群体，并使用盖亚eDR3验证了他们的成员身份。 我们报告了在我们分析的视线方向内偶然发现的11个新的开放星团候选者。 我们介绍了我们的技术、其优势和挑战，以及发布了我们的成员列表和更新的星团参数。 显示英文摘要

摘要： Open clusters are groups of stars that form at the same time, making them an ideal laboratory to test theories of star formation, stellar evolution, and dynamics in the Milky Way disk. However, the utility of an open cluster can be limited by the accuracy and completeness of its known members. Here, we employ a "top-down" technique, {\it extreme deconvolution gaussian mixture models} (XDGMM), to extract and evaluate known open clusters from Gaia DR2 by fitting the distribution of stellar parallax and proper motion along a line-of-sight. Extreme deconvolution techniques can recover the intrinsic distribution of astrometric quantities, accounting for the full covariance matrix of the errors; this allows open cluster members to be identified even when presented with relatively uncertain measurement data. To date, open cluster studies have only applied extreme deconvolution to specialized searches for individual systems. We use XDGMM to characterize the open clusters reported by Ahumada et al. 2007 and are able to recover 420 of the 426 open clusters therein (98.1\%). Our membership list contains the overwhelming majority ($>95\%$) of previously known cluster members. We also identify a new, significant, and relatively faint cluster member population and validate their membership status using Gaia eDR3. We report the fortuitous discovery of 11 new open cluster candidates within the lines of sight we analyzed. We present our technique, its advantages and challenges, as well as publish our membership lists and updated cluster parameters.