pySPT：专用于源位置转换的软件包
pySPT: a package dedicated to the source position transformation
摘要：
现代延时宇宙学的目标是通过观察多重成像的类星体来以具有竞争力的精度推断宇宙学参数。该技术的成功依赖于晶状体质量分布的稳健建模。不幸的是，导致几乎相同的透镜观测值的密度分布之间的强简并可能会使哈勃常数的精确估计产生偏差。源位置变换（SPT），它涵盖了著名的质量片变换（MST）作为一个特殊的情况下，定义了一个新的框架来调查这些退化。在本文中，我们介绍了pySPT，一个专用于SPT的Python包。我们描述了它可以用来评估的SPT透镜观测的影响。我们回顾了它的大部分功能，并详细介绍了我们在关于SPT和时间延迟的配套文件中使用的关键功能。pySPT还附带了一个专用于简单镜头建模的子包。它可用于生成各种透镜模型的透镜相关量，独立于任何SPT分析。作为第一个实际应用，我们提出了一个修正的SPT的时间延迟的影响，这已经在实验中发现Schneider和Sluse（2013）之间的软化幂律和复合（重子+暗物质）透镜的第一个估计。我们发现Schneider和Sluse（2014）中预测的大偏差被高估了，这是由于公共镜片建模代码lensmodel（v1.99）中的一个小错误（现已修复）。我们的结论是，哈勃常数的预测偏离$\sim 7$ \%，首先是由MST造成的。pySPT的最新版本可在https://github.com/owertz/pySPT上获得。我们还提供教程，详细描述如何最好地利用pySPT，网址为https://github.com/owertz/pySPT_tutorials。
英文摘要：
The 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 estimate for 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. pySPT also comes with a sub-package dedicated to simple lens modeling. It can be used to generate lensing related quantities for a wide variety of lens models, independently from 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 Schneider and Sluse (2013) between a softened power-law and a composite (baryons + dark matter) lenses. We find that the large deviations predicted in Schneider and Sluse (2014) have been overestimated due to a minor bug (now fixed) in the public lens modeling code lensmodel (v1.99). We conclude that the predictions for the Hubble constant deviate by $\sim 7$\%, first and foremost caused by an MST. The latest version of pySPT is available at https://github.com/owertz/pySPT. We also provide tutorials to describe in detail how making the best use of pySPT at https://github.com/owertz/pySPT_tutorials.
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