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PyAutoLens: Strong lens modeling

dc.contributor.authorNightingale, J. W.
dc.contributor.authorHayes, R.
dc.contributor.authorDye, S.
dc.contributor.authorMassey, R. J.
dc.contributor.authorFrenk, C. S.
dc.date.accessioned2023-12-07T09:45:45Z
dc.date.available2023-12-07T09:45:45Z
dc.date.issued2018-07
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/10948
dc.description2018ascl.soft07003Nen_UK
dc.description.abstractPyAutoLens models and analyzes galaxy-scale strong gravitational lenses. This automated module suite simultaneously models the lens galaxy's light and mass while reconstructing the extended source galaxy on an adaptive pixel-grid. Source-plane discretization is amorphous, adapting its clustering and regularization to the intrinsic properties of the lensed source. The lens's light is fitted using a superposition of Sersic functions, allowing PyAutoLens to cleanly deblend its light from the source. Bayesian model comparison is used to automatically chose the complexity of the light and mass models. PyAutoLens provides accurate light, mass, and source profiles inferred for data sets representative of both existing Hubble imaging and future Euclid wide-field observations.en_UK
dc.language.isoenen_UK
dc.publisherAstrophysics Source Code Libraryen_UK
dc.relation.urihttps://ascl.net/code/search/2018ascl.soft07003Nen_UK
dc.rightscontact researcher for access conditions*
dc.rights.urihttp://rdmc.nottingham.ac.uk/static/contact_for_licence.pdf*
dc.titlePyAutoLens: Strong lens modelingen_UK
dc.typedataset
dc.identifier.doihttp://doi.org/10.17639/nott.7356
dc.subject.freeGalaxy, Gravitational lenses, Data sets, Hubble Space Telescope, Euclid wide-field observationsen_UK
dc.subject.lcQ Science::QB Astronomyen_UK
uon.divisionUniversity of Nottingham, UK Campus::Faculty of Science::School of Physics and Astronomyen_UK
uon.funder.controlledOtheren_UK
uon.datatypeSoftwareen_UK
uon.collectionmethodBayesianen_UK


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