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arXiv 1905.09802astro-ph.CO
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MIFAL:用于强透镜建模的全自动多图像投影仪--概念验证

MIFAL: fully automated Multiple-Image Finder ALgorithm for strong-lens modelling -- proof of concept

Mauricio Carrasco, Adi Zitrin, Gregor Seidel

摘要:

我们概述了一个简单的程序设计的\emph{automatically}寻找强透镜(SL)集群中的多个图像集。我们表明,通过结合(a)一个弧发现(或源提取)程序,(b)光度红移测量,和(c)一个初步的光迹质量透镜模型,多图像系统可以识别在一个完全自动化的(“盲”)的方式。所提出的程序产生的可能性,每个弧属于多图像系统之一,以及不同的系统的首选红移的评估。然后,这些可以用于自动约束和细化初始晶状体模型,以获得准确的质量分布。我们应用这个程序的\emph{Cluster Lensing And Supernova with Hubble}观测三个星系团,MACS J0329.6-0211,MACS J1720.2+3536,和MACS J1931.8-2635,比较结果发表的SL分析,多个图像进行了验证眼睛在特定的基础上。在第一个集群中,所有最初识别的系统都通过自动化程序恢复,在第二个和第三个集群中,大约有一半被恢复。其他已知的系统没有被选中,部分原因是参数的粗略选择,模糊的光度红移,或初始透镜模型的不准确。在恢复的真实系统之上,该过程还错误地识别了一些虚假图像,这取决于所使用的阈值。虽然进一步改进的程序和更彻底的审查其性能是必要的,工作构成了另一个重要的一步,完全自动化SL分析研究大集群样本的质量分布。

英文摘要:

We outline a simple procedure designed for \emph{automatically} finding sets of multiple images in strong lensing (SL) clusters. We show that by combining (a) an arc-finding (or source extracting) program, (b) photometric redshift measurements, and (c) a preliminary light-traces-mass lens model, multiple-image systems can be identified in a fully automated (`blind') manner. The presented procedure yields an assessment of the likelihood of each arc to belong to one of the multiple-image systems, as well as the preferred redshift for the different systems. These could be then used to automatically constrain and refine the initial lens model for an accurate mass distribution. We apply this procedure to \emph{Cluster Lensing And Supernova with Hubble} observations of three galaxy clusters, MACS J0329.6-0211, MACS J1720.2+3536, and MACS J1931.8-2635, comparing the results to published SL analyses where multiple images were verified by eye on a particular basis. In the first cluster all originally identified systems are recovered by the automated procedure, and in the second and third clusters about half are recovered. Other known systems are not picked up, in part due to a crude choice of parameters, ambiguous photometric redshifts, or inaccuracy of the initial lens model. On top of real systems recovered, some false images are also mistakenly identified by the procedure, depending on the thresholds used. While further improvements to the procedure and a more thorough scrutinisation of its performance are warranted, the work constitutes another important step toward fully automatising SL analyses for studying mass distributions of large cluster samples.

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