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arXiv 2303.08474astro-ph.GA
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一种基于机器学习的 Gaia DR3 中疏散星团成员判定工具

A machine learning-based tool for open cluster membership determination in Gaia DR3

M. G. J. van Groeningen, A. Castro-Ginard, A. G. A. Brown, L. Casamiquela, C. Jordi

AI总结:

本文提出一种基于深度神经网络的工具,利用 Gaia DR3 的五维天体测量和三个测光波段在高维空间中识别疏散星团暗弱成员(G>18),从而将成员列表扩展到 Gaia 极限星等,为质量、动力学研究和地面光谱巡天提供支持。

摘要:

利用 Gaia 数据(大多数使用 DR2)对疏散星团进行成员研究,由于暗端的天体测量不确定性,目前仅限于 G = 18 星等。我们的目标是将现有疏散星团成员列表扩展到暗弱成员,并刻画低质量端,这些成员对许多应用很重要,特别是对地面光谱巡天。我们使用深度神经网络架构来学习已知星团周围高可信疏散星团成员星的分布。之后,我们使用训练好的网络,根据它们在五维天体测量加三个测光波段构成的高维空间中的相似性来估计新的疏散星团成员。由于 Gaia DR3 相对于 DR2 在天体测量精度上的提高,我们能够对已知疏散星团群体均匀地探测到新的暗弱成员星(G > 18)。我们的方法可以为疏散星团提供扩展成员列表,直至 Gaia 的极限星等,这将使进一步研究能够刻画疏散星团群体,例如估计它们的质量或动力学。这些扩展成员列表也是即将开展的地面光谱巡天的理想目标列表。

英文摘要:

Membership studies characterising open clusters with Gaia data, most using DR2, are so far limited at magnitude G = 18 due to astrometric uncertainties at the faint end. Our goal is to extend current open cluster membership lists with faint members and to characterise the low-mass end, which members are important for many applications, in particular for ground-based spectroscopic surveys. We use a deep neural network architecture to learn the distribution of highly reliable open cluster member stars around known clusters. After that, we use the trained network to estimate new open cluster members based on their similarities in a high-dimensional space, five-dimensional astrometry plus the three photometric bands. Due to the improved astrometric precisions of Gaia DR3 with respect to DR2, we are able to homogeneously detect new faint member stars (G > 18) for the known open cluster population. Our methodology can provide extended membership lists for open clusters down to the limiting magnitude of Gaia, which will enable further studies to characterise the open cluster population, e.g. estimation of their masses, or their dynamics. These extended membership lists are also ideal target lists for forthcoming ground-based spectroscopic surveys.

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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