PyAutoLens: Strong lens modeling
Publication date
2018-07Creators
Nightingale, J. W.
Hayes, R.
Dye, S.
Massey, R. J.
Frenk, C. S.
Metadata
Show full item recordDescription
PyAutoLens 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.
External URI
Subjects
- Galaxy, Gravitational lenses, Data sets, Hubble Space Telescope, Euclid wide-field observations
- Q Science::QB Astronomy
Divisions
- University of Nottingham, UK Campus::Faculty of Science::School of Physics and Astronomy
Deposit date
2023-12-07Data type
SoftwareFunders
- Other
Data collection method
BayesianResource languages
- en