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A dedicated source-position transformation package: pySPT ?

机译:专用的源位置转换包: pySPT

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Modern time-delay cosmography aims to infer the cosmological parameters with a competitive precision from observing a multiply imaged quasar. The success of this technique relies upon a robust modeling of the lens mass distribution. Unfortunately strong degeneracies between density profiles that lead to almost the same lensing observables may bias precise estimates of the Hubble constant. The source position transformation (SPT), which covers the well-known mass-sheet transformation (MST) as a special case, defines a new framework to investigate these degeneracies. In this paper, we present pySPT, a python package dedicated to the SPT. We describe how it can be used to evaluate the impact of the SPT on lensing observables. We review most of its capabilities and elaborate on key features that we used in a companion paper regarding SPT and time delays. The pySPT program also comes with a subpackage dedicated to simple lens modeling. This can be used to generate lensing related quantities for a wide variety of lens models independent of any SPT analysis. As a first practical application, we present a correction to the first estimate of the impact on time delays of the SPT, which has been experimentally found in a previous work between a softened power law and composite (baryons + dark matter) lenses. We find that the large deviations previously predicted have been overestimated because of a minor bug in the public lens modeling code lensmodel (v1.99), which is now fixed. We conclude that the predictions for the Hubble constant deviate by ~7%, first and foremost as a consequence of an MST. The latest version of pySPT is available on Github, a software development platform, along with some tutorials to describe in detail how making the best use of pySPT .
机译:现代时延宇宙学旨在通过观察多重成像的类星体,以具有竞争性的精度来推断宇宙学参数。该技术的成功依赖于镜片质量分布的可靠建模。不幸的是,导致几乎相同的镜头可观察物的密度分布之间的强烈简并性可能会使哈勃常数的精确估计产生偏差。源位置转换(SPT)作为特例涵盖了著名的质量表转换(MST),它定义了研究这些简并性的新框架。在本文中,我们介绍了pySPT,这是专用于SPT的python软件包。我们描述了如何将其用于评估SPT对可观察镜头的影响。我们回顾了它的大多数功能,并详细介绍了我们在随附论文中使用的有关SPT和时间延迟的关键功能。 pySPT程序还附带一个子包,专门用于简单的镜头建模。这可用于为各种镜头模型生成与镜头有关的数量,而与任何SPT分析无关。作为第一个实际应用,我们提出了对SPT时延影响的第一个估计值的校正,该估计值是在以前的工作中通过实验发现的,在软化幂律和复合(重子+暗物质)透镜之间。我们发现,由于公共镜头建模代码lensmodel(v1.99)中的一个小错误,先前预测的大偏差被高估了,该错误现已修复。我们得出的结论是,首先和最重要的是,由于MST,哈勃常数的预测偏差了约7%。 pySPT的最新版本可在软件开发平台Github上获得,同时还提供了一些教程来详细描述如何充分利用pySPT。

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