A genetic algorithm for the non-parametric inversion of strong lensing systems

Liesenborgs, Jori; Rijcke, S. De; Dejonghe, Herwig

doi:10.1111/j.1365-2966.2006.10040.x

Cited by 117 publications

(131 citation statements)

References 25 publications

(29 reference statements)

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…Saha & Williams 2004;Diego et al 2005;Liesenborgs et al 2006;Coe et al 2008) fit the lensing observables perfectly and derive ensembles of models reproducing existing data for sets of individual multiplyimaged sources. These techniques effectively explore degeneracies between lens models (Saha & Williams 2001, 2006, but with the drawback that many non-physical models (e.g., dynamically unstable or with arbitrary substructure) can be obtained.…”

Section: Discussionmentioning

confidence: 99%

Source-position transformation: an approximate invariance in strong gravitational lensing

Schneider

Sluse

2014

A&A

149

146

View full text Add to dashboard Cite

The main obstacle that gravitational lensing has in determining accurate masses of deflectors, or in determining precise estimates for the Hubble constant, is the degeneracy of lensing observables with respect to the mass-sheet transformation (MST). The MST is a global modification of the mass distribution which leaves all image positions, shapes, and flux ratios invariant, but which changes the time delay. Here we show that another global transformation of lensing mass distributions exists which leaves image positions and flux ratios almost invariant, and of which the MST is a special case. As is the case for the MST, this new transformation only applies if one considers only those source components that are at the same distance from us. Whereas for axi-symmetric lenses this source position transformation exactly reproduces all strong lensing observables, it does so only approximately for more general lens situations. We provide crude estimates for the accuracy with which the transformed mass distribution can reproduce the same image positions as the original lens model, and present an illustrative example of its performance. This new invariance transformation is most likely the reason why the same strong lensing information can be accounted for with rather different mass models.

show abstract

Section: Discussionmentioning

confidence: 99%

Source-position transformation: an approximate invariance in strong gravitational lensing

Schneider

Sluse

2014

A&A

149

146

View full text Add to dashboard Cite

show abstract

“…Two of these parameters are primarily set to reasonable values which means that only four of these parameters have to be constrained initially, which sets a very reliable starting-point using obvious systems. Recently we have further tested this assumption with Abell 1703 (Zitrin et al 2010), where we also applied the non-parametric technique of Liesenborgs et al (2006) for comparison. This latter technique employs an adaptive grid inversion method and does not make any prior assumptions of the mass distribution.…”

Section: Strong-lensing Analysis Of the Macs High-redshift Cluster Samentioning

confidence: 99%

Comparison of an X-ray-selected sample of massive lensing clusters with the MareNostrum Universe ΛCDM simulation

Meneghetti

Fedeli

Zitrin

et al. 2011

A&A

123

View full text Add to dashboard Cite

Context. A long-standing problem of strong-lensing by galaxy clusters is the observed high rate of giant gravitational arcs that are not predicted in the framework of the "standard" cosmological model. This is known as the "arc statistics problem". Recently, several other inconsistencies between the theoretical expectations and observations have been claimed regarding the large size of the Einstein rings and the high concentrations of few clusters with strong-lensing features. All these problems consistently indicate that observed galaxy clusters may be stronger gravitational lenses than expected. Aims. We aim at better understanding these problems by comparing the lensing properties of well defined cluster samples with those of a large set of numerically simulated objects. Methods. We use clusters extracted from the MareNostrum Universe to build up mock catalogs of galaxy clusters selected through their X-ray flux. We use these objects to estimate the probability distributions of lensing cross sections, Einstein rings, and concentrations for a sample of 12 MACS clusters at z > 0.5 from the literature. Results. We find that three clusters in the MACS sample have lensing cross sections and Einstein ring sizes larger than any simulated cluster in the MareNostrum Universe. We use the lensing cross sections of simulated and real clusters to estimate the number of giant arcs that should arise from lensed sources at z = 2. We find that simulated clusters produce ∼50% less arcs than observed clusters do. The medians of the distributions of the Einstein ring sizes differ by ∼25% between simulations and observations. We estimate that the concentrations of the individual MACS clusters inferred from the lensing analysis should be up to a factor of ∼2 larger than expected from the ΛCDM model because of cluster triaxiality and orientation biases that affect the lenses with the largest cross sections. In particular, we predict that for ∼20% of the clusters in the MACS sample the lensing-derived concentrations should be higher than expected by more than ∼40%. Conclusions. The arc statistics, the Einstein ring, and the concentration problems in strong lensing clusters are mitigated but not solved on the basis of our analysis. Nevertheless, owing to the lack of redshifts for most of the multiple image systems used for modeling the MACS clusters, the results of this work will need to be verified with additional data. The upcoming CLASH program will provide an ideal sample for extending our comparison.

show abstract

“…Genetic algorithms are often applied to optimize noisy objective functions (Metcalfe et al 2000;Larsen & Humphreys 2003). During the past decade, genetic algorithms have become increasingly more popular in numerous applications in astronomy and astrophysics ranging from cosmology and gravitational lens modeling to stellar structure and spectral fitting (Charbonneau 1995;Metcalfe et al 2000;Theis & Kohle 2001;Larsen & Humphreys 2003;Fletcher et al 2003;Liesenborgs et al 2006;Baier et al 2010;Schechtman-Rook et al 2012;Rajpaul 2012a). For a recent overview of the use of genetic algorithms in astronomy and astrophysics, see Rajpaul (2012b).…”

Section: Introductionmentioning

confidence: 99%

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

Geyter

Baes

Fritz

et al. 2013

A&A

View full text Add to dashboard Cite

We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi-dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC 4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.

show abstract

A genetic algorithm for the non-parametric inversion of strong lensing systems

Cited by 117 publications

References 25 publications

Source-position transformation: an approximate invariance in strong gravitational lensing

Source-position transformation: an approximate invariance in strong gravitational lensing

Comparison of an X-ray-selected sample of massive lensing clusters with the MareNostrum Universe ΛCDM simulation

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

Contact Info

Product

Resources

About