论文标题
比较模拟的银河系卫星星系和使用无监督聚类的观测
Comparing simulated Milky Way satellite galaxies with observations using unsupervised clustering
论文作者
论文摘要
我们开发了一种新的分析方法，该方法使我们能够将多维可观察物与理论模型进行比较。该方法基于无监督的聚类算法，该算法将观测数据和模拟数据分配给高维度的集群。从聚类结果中，通过Fisher-Freeman-Halton测试确定拟合良好（P值）。我们首先表明这种方法对于2D高斯分布是强大的。然后，我们将该方法应用于观察到的MW卫星和我们半分析代码A-Sloth的基准模型中的模拟卫星。我们在分析中使用以下5个观察到星系的观察值：恒星质量，病毒质量，中心距离，平均恒星金属性[Fe/H]和恒星金属性色散σ[Fe/H]。从分析中返回的低p值告诉我们，我们的A-斜骨基准模型不能很好地再现观察到的MW卫星的平均恒星金属性。我们对物理模型实施了临时改进，并表明具有p值> 0.01的暗物质合并树的数量从3增加到6。此方法可以扩展到具有较高维度的数据。我们计划使用这种方法在观察到的MW卫星中研究恒星的元素丰度，以进一步改善A-Sloth的物理模型。
We develop a new analysis method that allows us to compare multi-dimensional observables to a theoretical model. The method is based on unsupervised clustering algorithms which assign the observational and simulated data to clusters in high dimensionality. From the clustering result, a goodness of fit (the p-value) is determined with the Fisher-Freeman-Halton test. We first show that this approach is robust for 2D Gaussian distributions. We then apply the method to the observed MW satellites and simulated satellites from the fiducial model of our semi-analytic code A-SLOTH. We use the following 5 observables of the galaxies in the analysis: stellar mass, virial mass, heliocentric distance, mean stellar metallicity [Fe/H], and stellar metallicity dispersion σ[Fe/H]. A low p-value returned from the analysis tells us that our A-SLOTH fiducial model does not reproduce the mean stellar metallicity of the observed MW satellites well. We implement an ad-hoc improvement to the physical model and show that the number of dark matter merger trees which have p-values > 0.01 increases from 3 to 6. This method can be extended to data with higher dimensionality easily. We plan to further improve the physical model in A-SLOTH using this method to study elemental abundances of stars in the observed MW satellites.
