TDCOSMO: X. Automated modeling of nine strongly lensed quasars and comparison between lens-modeling software
TDCOSMO: X. Automated modeling of nine strongly lensed quasars and comparison between lens-modeling software
复制标题
TDCOSMO：X. 九个强透镜类星体的自动建模以及透镜建模软件之间的比较
DOI：
10.1051/0004-6361/202244909
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发表时间：
2023
影响因子：
6.5
通讯作者：
Sluse, D.
中科院分区：
文献类型：
--
作者：
Ertl, S.;Schuldt, S.;Suyu, S. H.;Schmidt, T.;Treu, T.;Birrer, S.;Shajib, A. J.;Sluse, D.
当强引力透镜被用作天体物理学或宇宙学探测器时，通常需要它们的质量分布模型。我们提出了一个新的，时间有效的自动化代码的统一建模的强透镜类星体与GLEE，透镜建模软件的多波段数据。利用透镜类星体的观测位置和主星系表面亮度分布的空间扩展，我们得到了一个透镜星系质量分布的模型。我们将这种统一的建模管道应用于9个强透镜类星体的样本，这些类星体的图像是用哈勃太空望远镜的宽视场相机3获得的。该模型显示良好的重建轻组件和质量和轻的质心之间的良好对齐在大多数情况下。我们发现，自动建模代码显着减少了输入时间在建模过程中的用户。准备所需输入文件的时间减少了3倍，从约3小时减少到约1小时。用户在建模过程中的有效输入时间减少了10倍，从每个透镜系统的约10小时减少到约1小时。这种自动化的统一建模管道可以有效地产生广泛的透镜系统样本的统一模型，可用于进一步的宇宙学分析。将我们的结果与基于建模软件Lenstronomy的独立自动建模管道的结果进行了盲测，揭示了重要的教训。诸如爱因斯坦半径、天体测量、质量扁平化和位置角之类的参数通常是鲁棒确定的。其他量，例如质量密度分布的径向斜率和预测的时间延迟，在很大程度上取决于数据的质量和重建点扩散函数的准确度。更好的数据和/或更详细的分析是将我们的自动化模型提升到宇宙学级别所必需的。尽管如此，我们的管道可以快速选择镜头进行后续和进一步建模，这大大加快了宇宙学级模型的构建。这一重要的进步将有助于我们利用未来十年预计将增加的镜头数量，这是几个数量级的增长。
When strong gravitational lenses are to be used as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for the uniform modeling of strongly lensed quasars with GLEE, a lens-modeling software for multiband data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the host galaxy of the lensed quasar, we obtain a model of the mass distribution of the lens galaxy. We applied this uniform modeling pipeline to a sample of nine strongly lensed quasars for which images were obtained with the Wide Field Camera 3 of theHubbleSpace Telescope. The models show well-reconstructed light components and a good alignment between mass and light centroids in most cases. We find that the automated modeling code significantly reduces the input time during the modeling process for the user. The time for preparing the required input files is reduced by a factor of 3 from ~3 h to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ~ 10 h to about one hour per lens system. This automated uniform modeling pipeline can efficiently produce uniform models of extensive lens-system samples that can be used for further cosmological analysis. A blind test that compared our results with those of an independent automated modeling pipeline based on the modeling software Lenstronomy revealed important lessons. Quantities such as Einstein radius, astrometry, mass flattening, and position angle are generally robustly determined. Other quantities, such as the radial slope of the mass density profile and predicted time delays, depend crucially on the quality of the data and on the accuracy with which the point spread function is reconstructed. Better data and/or a more detailed analysis are necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up and further modeling, which significantly speeds up the construction of cosmography-grade models. This important step forward will help us to take advantage of the increase in the number of lenses that is expected in the coming decade, which is an increase of several orders of magnitude.