A model-independent characterisation of strong gravitational lensing by observables
When light from a distant source object, like a galaxy or a supernova, travels towards us, it is deflected by massive objects that lie in its path. When the mass density of the deflecting object exceeds a certain threshold, multiple, highly distorted images of the source are observed. This strong gr...
Saved in:
| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
23 July 2019
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| In: |
Universe
Year: 2019, Volume: 5, Issue: 7 |
| ISSN: | 2218-1997 |
| DOI: | 10.3390/universe5070177 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3390/universe5070177 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2218-1997/5/7/177 
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| Author Notes: | Jenny Wagner |
MARC
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| 520 | |a When light from a distant source object, like a galaxy or a supernova, travels towards us, it is deflected by massive objects that lie in its path. When the mass density of the deflecting object exceeds a certain threshold, multiple, highly distorted images of the source are observed. This strong gravitational lensing effect has so far been treated as a model-fitting problem. Using the observed multiple images as constraints yields a self-consistent model of the deflecting mass density and the source object. As several models meet the constraints equally well, we develop a lens characterisation that separates data-based information from model assumptions. The observed multiple images allow us to determine local properties of the deflecting mass distribution on any mass scale from one simple set of equations. Their solution is unique and free of model-dependent degeneracies. The reconstruction of source objects can be performed completely model-independently, enabling us to study galaxy evolution without a lens-model bias. Our approach reduces the lens and source description to its data-based evidence that all models agree upon, simplifies an automated treatment of large datasets, and allows for an extrapolation to a global description resembling model-based descriptions. | ||
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