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arXiv · 1801.04151

pySPT: a package dedicated to the source position transformation

Olivier Wertz·Bastian Orthen
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Abstract

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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Olivier Wertz, Bastian Orthen. 2018-01-12. pySPT: a package dedicated to the source position transformation. https://doi.org/10.1051/0004-6361%2F201732242

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