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handle: 11585/813213
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.
2 pages, 1 figure
Astrophysics of Galaxies (astro-ph.GA), FOS: Physical sciences, Astrophysics - Instrumentation and Methods for Astrophysics, software, lensing, strong lensing, Astrophysics - Astrophysics of Galaxies, Instrumentation and Methods for Astrophysics (astro-ph.IM), 520
Astrophysics of Galaxies (astro-ph.GA), FOS: Physical sciences, Astrophysics - Instrumentation and Methods for Astrophysics, software, lensing, strong lensing, Astrophysics - Astrophysics of Galaxies, Instrumentation and Methods for Astrophysics (astro-ph.IM), 520
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 60 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |