论文标题
罗杰：使用机器学习技术在极端地区重建星系的轨道
ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
论文作者
论文摘要
我们根据其预计的相位空间位置，使用三种不同的机器学习技术来对Roger（极端区域中的重建轨道进行重建轨道）代码，该代码使用三种不同的机器学习技术来对星系群进行分类。我们使用34个巨大的样本，$ m_ {200}> 10^{15} h^{ - 1} m _ {\ odot} $，在红色Shift Zero处的Multidark Planck 2（MDLP2）模拟中的Galaxy簇。我们选择所有带有恒星质量的星系$ m _ {\ star} \ ge 10^{8.5} h^{ - 1} m _ {\ odot} $，该模型由星系sag的半分析模型计算出来，该模型位于及其cluster and clusters and cluster and clusity and clusity and clusity and or berbits中，并根据其或分类。我们训练罗杰（Roger）从其预计的相位空间位置中检索出星系的原始分类。对于每个星系，罗杰（Roger）将作为群集星系的概率，最近落入群集，后挡板星系，插入星系或interloper的概率。我们讨论了机器学习方法的性能和代码的潜在用途。在探索的不同方法中，我们发现K-Neart最邻居算法实现了最佳性能。
We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We use a sample of 34 massive, $M_{200}>10^{15} h^{-1} M_{\odot}$, galaxy clusters in the MultiDark Planck 2 (MDLP2) simulation at redshift zero. We select all galaxies with stellar mass $M_{\star} \ge 10^{8.5} h^{-1}M_{\odot}$, as computed by the semi-analytic model of galaxy formation SAG, that are located in, and in the vicinity of, the clusters and classify them according to their orbits. We train ROGER to retrieve the original classification of the galaxies out of their projected phase-space positions. For each galaxy, ROGER gives as output the probability of being a cluster galaxy, a galaxy that has recently fallen into a cluster, a backsplash galaxy, an infalling galaxy, or an interloper. We discuss the performance of the machine learning methods and potential uses of our code. Among the different methods explored, we find the K-Nearest Neighbours algorithm achieves the best performance.
