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HierArc

Hierarchical inference of cosmological parameters from a set of strong lensing systems

Install / Use

/learn @sibirrer/HierArc
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

======= hierArc

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Hierarchical analysis of strong lensing systems to infer lens properties and cosmological parameters simultaneously.

The software is originated from Birrer et al. 2020 <https://arxiv.org/abs/2007.02941>_ and is in active development.

  • Free software: BSD license
  • Documentation: https://hierarc.readthedocs.io.

Features

The software allows to fit lenses with measured time delays, imaging information, kinematics constraints and standardizable magnifications with parameters described on the ensemble level.

Installation

.. code-block:: bash

$ pip install hierarc --user

Usage

The full analysis of Birrer et al. 2020 <https://arxiv.org/abs/2007.02941>_ is publicly available at this TDCOSMO repository <https://github.com/TDCOSMO/hierarchy_analysis_2020_public>_ . A forecast based on hierArc is presented by Birrer & Treu 2020 <https://arxiv.org/abs/2008.06157>_ and the notebooks are available at this repository <https://github.com/sibirrer/TDCOSMO_forecast>. The extension to using hierArc with standardizable magnifications is presented by Birrer et al. 2021 <https://arxiv.org/abs/2107.12385> and the forecast analysis is publicly available here <https://github.com/sibirrer/glSNe>_. For example use cases we refer to the notebooks of these analyses.

Credits

Simon Birrer & the TDCOSMO <http://tdcosmo.org>_ team.

Please cite Birrer et al. 2020 <https://arxiv.org/abs/2007.02941>_ if you make use of this software for your research.

View on GitHub
GitHub Stars10
CategoryDevelopment
Updated1mo ago
Forks13

Languages

Python

Security Score

80/100

Audited on Feb 17, 2026

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