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lenstronomy II: A gravitational lensing software ecosystem

Simon Birrer, Anowar J. Shajib, Daniel Gilman, Aymeric Galan, Jelle Aalbers, Martin Millon, Robert Morgan, Giulia Pagano, Ji Won Park, Luca Teodori, Nicolas Tessore, Madison Ueland, Lyne Van de Vyvere, Sebastian Wagner-Carena, Ewoud Wempe, Lilan Yang, Xuheng Ding, Thomas Schmidt, Dominique Sluse, Ming Zhang, Adam Amara

arXiv:2106.05976v1astro-ph.COastro-ph.GAastro-ph.IM

TL;DR

Strong-lensing analyses address questions in cosmology and astrophysics, while anticipated survey growth increases the demand for reliable, reproducible software. This paper describes lenstronomy’s evolving modular software and its use across observational and theoretical studies. The reported record includes more than 30 peer-reviewed publications and a community ecosystem of affiliated packages.

Abstract

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lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the linear basis set approach by Birrer et a. (2015). The user and developer base of lenstronomy has substantially grown since then, and the software has become an integral part of a wide range of recent analyses, such as measuring the Hubble constant with time-delay strong lensing or constraining the nature of dark matter from resolved and unresolved small scale lensing distortion statistics. The modular design has allowed the community to incorporate innovative new methods, as well as to develop enhanced software and wrappers with more specific aims on top of the lenstronomy API. Through community engagement and involvement, lenstronomy has become a foundation of an ecosystem of affiliated packages extending the original scope of the software and proving its robustness and applicability at the forefront of the strong gravitational lensing community in an open source and reproducible manner.

Background

Strong gravitational lensing produces multiple distorted images when a massive foreground object lenses a background source. Its observations support research across cosmology and astrophysics.

Background

  • A massive foreground object can lens a background galaxy or quasar into multiple highly distorted images.Observed data also reflect the instrument, observing conditions, and noise.Source: Background · p. 2
  • Time delays between multiple images of variable sources provide precision measurements of the Universe’s expansion rate.Strong-lensing time delays also feature in studies of the Hubble constant.Source: Background · p. 2
  • Resolved-source distortions and unresolved flux ratios constrain the nature of dark matter.Source: Background · p. 2

Statement of need

Upcoming surveys are expected to expand lens samples substantially, increasing their potential for statistical precision and discovery. The paper presents lenstronomy’s modular, evolving design and user support as a response to demanding analyses that need reliable and reproducible software.

Statement of need

  • Next-generation imaging and interferometric surveys are expected to increase the number of discovered lenses by more than an order of magnitude.Large samples can provide unprecedented statistical precision and discovery potential.Source: Statement of need · pp. 2–3
  • The paper calls for reliable software and open-source, reproducible analyses to support demanding studies and transparent evidence.User guidance includes documentation, example notebooks, and publicly available end-to-end pipelines.Source: Statement of need · p. 3
  • lenstronomy’s modular API has accommodated new features as the software evolved around demanding strong-lensing modeling and inference applications.The stated design goal is to facilitate scientific investigations in cosmology and astrophysics.Source: Statement of need · p. 3

Track-record of applications

Since its 2018 public release, lenstronomy has been used across a wide range of observational and theoretical strong-lensing studies. Its applications include analyses of real data, forecasts, robustness studies, and a blind modeling challenge.

Track-record of applications

  • More than 30 peer-reviewed publications have used or contributed to lenstronomy since its first public release in 2018.Source: Track-record of applications · p. 3
  • Applications span Hubble-constant measurements, dark-matter inference, quasar and host-light decomposition, high-redshift morphology, galaxy structure, and weak-lensing measurements.Some studies applied pipelines to analyze dozens of lenses uniformly, described as a milestone toward using thousands.Source: Track-record of applications · p. 3
  • Theoretical studies used lenstronomy to investigate statistical robustness and forecast constraints for different science cases.Three separate teams used lenstronomy in the blind time-delay lens modeling challenge.Source: Track-record of applications · p. 3
  • The software incorporated innovations and numerical recipes and found applications beyond its original aim, attributed to its robust, high-standard design requirements.Source: Track-record of applications · pp. 3–4

Ecosystem of affiliated packages

Community-developed affiliated packages build on lenstronomy’s core functionality to provide targeted solutions for varied scientific analyses. Their modules can be combined without duplicating source code.

Ecosystem of affiliated packages

  • Affiliated packages use lenstronomy’s core functionality to provide more targeted and integrated software solutions for scientific analyses.Source: Ecosystem of affiliated packages · p. 4
  • The ecosystem includes tools for source reconstruction, gravitational-wave lensing, automated modeling, simulations, subhalo rendering, morphology analysis, and Hubble-constant inference.Examples include SLITronomy, lensingGW, dolphin, pyHalo, galight, and hierArc.Source: Ecosystem of affiliated packages · p. 4
  • Affiliated packages reuse lenstronomy modules without duplicating source code, allowing aspects of multiple packages to be combined in one analysis.Source: Ecosystem of affiliated packages · p. 4

Related open source software

The section lists six related open-source software projects.

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