PyAutoLens: Open-Source Strong Gravitational Lensing

التفاصيل البيبلوغرافية
العنوان: PyAutoLens: Open-Source Strong Gravitational Lensing
المؤلفون: Nightingale, James. W., Hayes, Richard G., Kelly, Ashley, Amvrosiadis, Aristeidis, Etherington, Amy, He, Qiuhan, Li, Nan, Cao, XiaoYue, Frawley, Jonathan, Cole, Shaun, Enia, Andrea, Frenk, Carlos S., Harvey, David R., Li, Ran, Massey, Richard J., Negrello, Mattia, Robertson, Andrew
المصدر: Journal of Open Source Software, 6(58), 2525 (2021)
سنة النشر: 2021
المجموعة: Astrophysics
مصطلحات موضوعية: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
الوصف: Strong gravitational lensing, which can make a background source galaxy appears multiple times due to its light rays being deflected by the mass of one or more foreground lens galaxies, provides astronomers with a powerful tool to study dark matter, cosmology and the most distant Universe. PyAutoLens is an open-source Python 3.6+ package for strong gravitational lensing, with core features including fully automated strong lens modeling of galaxies and galaxy clusters, support for direct imaging and interferometer datasets and comprehensive tools for simulating samples of strong lenses. The API allows users to perform ray-tracing by using analytic light and mass profiles to build strong lens systems. Accompanying PyAutoLens is the autolens workspace (see https://github.com/Jammy2211/autolens_workspace), which includes example scripts, lens datasets and the HowToLens lectures in Jupyter notebook format which introduce non experts to strong lensing using PyAutoLens. Readers can try PyAutoLens right now by going to the introduction Jupyter notebook on Binder (see https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/master) or checkout the readthedocs (see https://pyautolens.readthedocs.io/en/latest/) for a complete overview of PyAutoLens's features.
Comment: 2 pages, 1 figure
نوع الوثيقة: Working Paper
DOI: 10.21105/joss.02825
URL الوصول: http://arxiv.org/abs/2106.01384
رقم الأكسشن: edsarx.2106.01384
قاعدة البيانات: arXiv