论文标题
重新安装：重建来自重力镜头的星系簇的质量谱图
relensing: Reconstructing the mass profile of galaxy clusters from gravitational lensing
论文作者
论文摘要
在这项工作中，我们提出了一个用Python编写的软件包，其目标是从重力镜头中对星系簇进行建模。通过重新安装，我们扩展了可用的软件量，该软件为科学界提供了各种模型，可以帮助比较并验证依赖于它们的物理结果。我们实施了一种自由形式的方法，该方法可以在自适应不规则网格上计算重力偏转电位，从中可以将簇及其性质描述为重力透镜。在这里，我们使用两个替代惩罚功能来限制强镜头。我们对两个玩具型号进行了重新介绍，以探索在哪些条件下可以在重建中获得更好的性能。我们发现，通过将平滑性应用于偏转电位，我们能够提高这种方法的能力，以恢复Galaxy簇的质量分布的形状和大小及其放大图。这可以更好地估计临界曲线和苛刻曲线。平滑提供的功率还在模拟簇ARES和HERA上进行了测试，为此，我们分别在〜0.17 arcsec和〜0.16 arcsec的镜头平面上获得RMS。我们的结果代表了对重建的改进，这些重建具有与重新启示的性质相同的方法。同时，平滑性还提高了我们实施的稳定性，并减少了计算时间。在目前的状态下，可应要求提供。
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.
