Accurate Identification of Galaxy Mergers with Stellar Kinematics
Accurate Identification of Galaxy Mergers with Stellar Kinematics
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DOI：
10.3847/1538-4357/abe2a9
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
R. Nevin;L. Blecha;J. Comerford;J. Greene;D. Law;D. Stark;K. Westfall;J. A. Vázquez-Mata;
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文献类型：
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作者：
R. Nevin;L. Blecha;J. Comerford;J. Greene;D. Law;D. Stark;K. Westfall;J. A. Vázquez-Mata;
为了确定星系合并对星系演化的重要性，有必要设计分类工具，以识别不同类型和阶段的星系合并。在此之前，使用GADGET-3/日出模拟合并星系和线性判别分析（LDA），我们创建了一个准确的合并星系分类器的基础上成像预测。在这里，我们开发了一个互补的工具，恒星运动预测的基础上，来自相同的模拟套件。我们设计模拟恒星速度和速度色散图，以模仿规范的映射附近Galerkin在Apache点（MANGA）积分场光谱（IFS）调查，并利用LDA创建一个分类，基于11个运动学预测的线性组合。分类随质量比显著变化;主要（次要）合并分类的平均统计准确率为80%（70%），精确度为90%（85%），召回率为75%（60%）。主要的合并是最好的确定的预测跟踪全球运动学特征，而次要的合并依赖于本地功能，跟踪二级恒星组件。虽然运动学分类不如成像分类准确，但运动学预测因子在识别聚结后合并方面更好。结合成像+运动学分类有可能从成像和IFS调查（如MaNGA）中揭示更完整的合并样本。我们注意到，由于用于训练分类器的模拟套件覆盖了有限范围的星系属性（即，星系是中等质量的，并且盘占主导地位），结果可能不适用于所有的MaNGA星系。
To determine the importance of merging galaxies to galaxy evolution, it is necessary to design classification tools that can identify the different types and stages of merging galaxies. Previously, using GADGET-3/SUNRISE simulations of merging galaxies and linear discriminant analysis (LDA), we created an accurate merging galaxy classifier based on imaging predictors. Here, we develop a complementary tool, based on stellar kinematic predictors, derived from the same simulation suite. We design mock stellar velocity and velocity dispersion maps to mimic the specifications of the Mapping Nearby Galaxies at Apache Point (MaNGA) integral field spectroscopy (IFS) survey, and utilize an LDA to create a classification, based on a linear combination of 11 kinematic predictors. The classification varies significantly with mass ratio; the major (minor) merger classifications have a mean statistical accuracy of 80% (70%), a precision of 90% (85%), and a recall of 75% (60%). The major mergers are best identified by predictors that trace global kinematic features, while the minor mergers rely on local features that trace a secondary stellar component. While the kinematic classification is less accurate than the imaging classification, the kinematic predictors are better at identifying post-coalescence mergers. A combined imaging + kinematic classification has the potential to reveal more complete merger samples from imaging and IFS surveys such as MaNGA. We note that since the suite of simulations used to train the classifier covers a limited range of galaxy properties (i.e., the galaxies are of intermediate mass, and disk-dominated), the results may not be applicable to all MaNGA galaxies.