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arXiv 1902.02680astro-ph.GAastro-ph.CO
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用机器学习方法预测SDSS DR7星系晕质量

Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach

Victor F. Calderon, Andreas A. Berlind

备注：
13 pages, 8 Figures, submitted to MNRAS
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摘要：

我们提出了一种机器学习（ML）的方法来预测星系的暗物质晕质量，实现了传统方法的性能改进。我们训练三个ML算法（\texttt{XGBoost}，随机森林和神经网络）预测晕质量使用一组合成的星系目录，该目录是通过在N体模拟中填充暗物质晕与星系，并匹配斯隆数字巡天（SDSS）中星系的聚类和联合分布属性。我们探讨了不同的星系和集团相关的属性与晕质量的相关性，并提取了一组九个功能，最有助于晕质量的预测。我们发现，从ML算法的质量预测是更准确的比那些从晕丰度匹配（\texttt{HAM}）或动态质量（\texttt{DYN}）估计。由于这种方法的危险在于我们的训练数据可能无法准确地代表真实的宇宙，因此我们探索了在与训练阶段使用的假设不同的假设下构建的合成目录上测试模型的效果。我们测试了各种模型与不同的方式填充暗物质晕，如增加卫星星系的速度偏差。我们确定，尽管在不同数据上的训练和测试可能导致预测质量的系统误差，但ML方法仍然比\texttt{HAM}或\texttt{DYN}产生更好的质量。最后，我们将训练好的模型应用于SDSS DR7的星系和星系群目录，并给出了由此产生的晕质量。

英文摘要：

We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural network) to predict halo masses using a set of synthetic galaxy catalogues that are built by populating dark matter haloes in N-body simulations with galaxies, and that match both the clustering and the joint-distributions of properties of galaxies in the Sloan Digital Sky Survey (SDSS). We explore the correlation of different galaxy- and group-related properties with halo mass, and extract the set of nine features that contribute the most to the prediction of halo mass. We find that mass predictions from the ML algorithms are more accurate than those from halo abundance matching (\texttt{HAM}) or dynamical mass (\texttt{DYN}) estimates. Since the danger of this approach is that our training data might not accurately represent the real Universe, we explore the effect of testing the model on synthetic catalogues built with different assumptions than the ones used in the training phase. We test a variety of models with different ways of populating dark matter haloes, such as adding velocity bias for satellite galaxies. We determine that, though training and testing on different data can lead to systematic errors in predicted masses, the ML approach still yields substantially better masses than either \texttt{HAM}or \texttt{DYN}. Finally, we apply the trained model to a galaxy and group catalogue from the SDSS DR7 and present the resulting halo masses.

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