论文标题
通过无监督的机器学习的金属贫困星的化学分析分析
Chemo-kinematic analysis of metal-poor stars with unsupervised machine learning
论文作者
论文摘要
金属贫困的恒星在理解星系形成和进化中起着进口作用。建立星系的早期合并的证据可能保留在恒星的丰度，运动学和轨道参数的分布中。在这项工作中，我们报告了对金属贫困样品（[fe/h] $ \ leq $ -1.0）恒星的持续化学化学分析的初步结果。我们通过无监督的机器学习（分层聚类，K-均值群集分析和相关矩阵）探索了化学和轨道数据。我们的最终目标是找到一种最佳方法，以分离源自合并事件的不同银河恒星种群和恒星群体，例如Gaia-cenceladus和Sequoia。
Metal-poor stars play an import role in the understanding of Galaxy formation and evolution. Evidence of the early mergers that built up the Galaxy might remain in the distributions of abundances, kinematics, and orbital parameters of the stars. In this work, we report on preliminary results of an on-going chemo-kinematic analysis of a sample of metal-poor ([Fe/H] $\leq$ -1.0) stars observed by the GALAH spectroscopic survey. We explored the chemical and orbital data with unsupervised machine learning (hierarchical clustering, k-means cluster analysis and correlation matrices). Our final goal is to find an optimal way to separate different Galactic stellar populations and stellar groups originating from merging events, such as Gaia-Enceladus and Sequoia.
