
arXiv: 1908.01158
ABSTRACT The number of strong lens systems is expected to increase significantly in ongoing and upcoming surveys. With an increase in the total number of such systems, we expect to discover many configurations that correspond to unstable caustics. In such cases, the instability can be used to our advantage for constraining the lens model. We have implemented algorithms for detection of different types of singularities in gravitational lensing. We apply our approach on a variety of lens models and then go on to test it with the inferred mass distribution for Abell 697 as an example application. We propose to represent lenses using A3-lines and singular points (A4 and D4) in the image plane. We propose this as a compact representation of complex lens systems that can capture all the details in a single snapshot.
Cosmology and Nongalactic Astrophysics (astro-ph.CO), FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Cosmology and Nongalactic Astrophysics (astro-ph.CO), FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
| selected citations These citations are derived from selected sources. 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). | 8 | |
| 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 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
