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arXiv 2201.10076astro-ph.CO
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relensing:从引力透镜重建星系团的质量分布

relensing: Reconstructing the mass profile of galaxy clusters from gravitational lensing

Daniel A. Torres-Ballesteros, Leonardo Castañeda

摘要:

在这项工作中,我们提出了relensing,一个用python编写的软件包,其目标是从引力透镜模型星系团。通过重新照明,我们扩展了可用软件的数量,为科学界提供了广泛的模型,有助于比较并验证依赖于它们的物理结果。我们实现了一个自由形式的方法,计算重力偏转潜力的自适应不规则网格,从中可以描述集群和它的属性作为一个引力透镜。在这里,我们使用两个替代的惩罚函数来约束强透镜。我们将再光照应用于两个玩具模型,以探索在何种条件下可以获得更好的重建性能。我们发现,通过应用平滑的偏转电位,我们能够增加这种方法的能力,以恢复的形状和大小的质量分布的星系团,以及其放大地图。这转化为对临界曲线和焦散曲线的更好估计。还在模拟的星团Ares和Hera上测试了平滑提供的功率,对于它们,我们在透镜平面上分别得到~0.17 arcsec和~0.16 arcsec的均方根。我们的结果代表了相对于使用与重新照明相同性质的方法进行的重建的改进。同时,平滑也增加了我们实现的稳定性,并减少了计算时间。在其当前状态下,可根据要求重新调整。

英文摘要:

In this work we present relensing, a package written in python whose goal is to model galaxy clusters from gravitational lensing. With relensing we extend the amount of software available, which provides the scientific community with a wide range of models that help to compare and therefore validate the physical results that rely on them. We implement a free-form approach which computes the gravitational deflection potential on an adaptive irregular grid, from which one can characterize the cluster and its properties as a gravitational lens. Here, we use two alternative penalty functions to constrain strong lensing. We apply relensing to two toy models, in order to explore under which conditions one can get a better performance in the reconstruction. We find that by applying a smoothing to the deflection potential, we are able to increase the capability of this approach to recover the shape and size of the mass profile of galaxy clusters, as well as its magnification map. This translates into a better estimation of the critical and caustic curves. The power that the smoothing provides is also tested on the simulated clusters Ares and Hera, for which we get an rms on the lens plane of ~0.17 arcsec and ~0.16 arcsec, respectively. Our results represent an improvement with respect to reconstructions that were carried out with methods of the same nature as relensing. At the same time, the smoothing also increases the stability of our implementation, and decreases the computation time. In its current state, relensing is available upon request.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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