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arXiv 1509.05409astro-ph.CO
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基于机器学习的污染星系团动力学质量测量

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning

M. Ntampaka, H. Trac, D. J. Sutherland, S. Fromenteau, B. Poczos, J. Schneider

摘要:

我们研究了动态质量测量的星系团污染的闯入者,并表明,现代机器学习(ML)算法可以预测质量比一个标准的比例关系方法相比,两个更好的因素。我们创建两个模拟目录从Multidark的公开可用的$N$体MDPL 1模拟,一个完美的星系团成员信息和其他一个简单的圆柱形切割周围的集群中心允许闯入者污染的集群。在标准的方法中,我们使用幂律标度关系来推断星系团的质量从星系视线(LOS)速度色散。假设完美的成员知识,这种不切实际的情况下产生一个宽的分数质量误差分布,宽度为$Δε\approx0.87$。闯入者引入了额外的散射,显著地进一步扩大了误差分布($Δε\approx2.13$)。我们采用支持分布机(SDM)类算法从数据分布中学习,以预测单个值。适用于分布的星系观测,如LOS速度和投影距离从集群中心,SDM产量优于一个因素的两个改善($Δε\approx0.67$)的污染情况。值得注意的是,SDM适用于污染的集群是更好地能够恢复质量比,甚至适用于未污染的集群的缩放关系的方法。我们发现,SDM方法更准确地再现集群质量函数,使其成为一个有价值的工具,采用集群观测来评估宇宙模型。

英文摘要:

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark's publicly available $N$-body MDPL1 simulation, one with perfect galaxy cluster membership information and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width of $Δε\approx0.87$. Interlopers introduce additional scatter, significantly widening the error distribution further ($Δε\approx2.13$). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement ($Δε\approx0.67$) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

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