论文标题
Astrolens：X射线选择的自动强镜模型选定的星系簇
AStroLens: Automatic Strong-Lens Modeling of X-ray Selected Galaxy Clusters
论文作者
论文摘要
我们使用Astrolens是一种新开发的重力透镜模型代码，仅依赖于群集星系的几何和光度信息作为输入，以绘制强镜区域，并估计96个星系簇（$ z = 0.5 $ 0.9 $）的96个星系簇的晶状体强度。所有簇均根据其X射线通量和光学外观确定在扩展的大型群集调查（EMAC）期间。基于经过良好测试的假设，即星系簇中发光和暗物质的分布大约通过光的分布来追踪，即光质量质量，Astrolens使用三个全局参数来自动建模该不同样品中所有Galaxy簇的强烈晶状体的偏转。我们通过比较仅从两个通带中的浅光学图像得出的Astrolens估计与对两个经过良好研究的EMAC群集的深入镜头模型工作的结果进行比较，以测试代码的鲁棒性。我们的研究发现31个Emacs群集具有有效的Einstein Radii（$θ_{E} $）超过20英寸，而8个带有$θ_{e}> $ 30英寸的emacs簇，从而突显了X射线选择的价值，以发现强大的集群巨人在Ever-MacSJ0717171717中，以此为互补的cluster lenses。根据Astrolens的说法，作为EMACS样本公开发布的第一部分，我们列出了十个校准簇的物理特性以及十个最强大的Emacs群集镜头的物理特性。
We use AStroLens, a newly developed gravitational lens-modeling code that relies only on geometric and photometric information of cluster galaxies as input, to map the strong-lensing regions and estimate the lensing strength of 96 galaxy clusters at $z=0.5$-$0.9$. All clusters were identified during the extended Massive Cluster Survey (eMACS) based on their X-ray flux and optical appearance. Building on the well tested assumption that the distribution of both luminous and dark matter in galaxy clusters is approximately traced by the distribution of light, i.e., that light traces mass, AStroLens uses three global parameters to automatically model the deflection from strong-gravitational lensing for all galaxy clusters in this diverse sample. We test the robustness of our code by comparing AStroLens estimates derived solely from shallow optical images in two passbands with the results of in-depth lens-modeling efforts for two well studied eMACS clusters and find good agreement, both with respect to the size and the shape of the strong-lensing regime delineated by the respective critical lines. Our study finds 31 eMACS clusters with effective Einstein radii ($θ_{E}$) in excess of 20" and eight with $θ_{E} >$ 30", thereby underlining the value of X-ray selection for the discovery of powerful cluster lenses that complement giants like MACSJ0717 at ever-increasing redshift. As a first installment toward the public release of the eMACS sample, we list physical properties of the ten calibration clusters as well as of the ten most powerful eMACS cluster lenses, according to AStroLens.
