When Darwin Met Einstein: Gravitational Lens Inversion with Genetic Algorithms

Abstract

Gravitational lensing can magnify a distant source, revealing structural detail which is normally unresolvable. Recovering this detail through an inversion of the influence of gravitational lensing, however, requires optimisation of not only lens parameters, but also of the surface brightness distribution of the source. This paper outlines a new approach to this inversion, utilising genetic algorithms to reconstruct the source profile. In this initial study, the effects of image degradation due to instrumental and atmospheric effects are neglected and it is assumed that the lens model is accurately known, but the genetic algorithm approach can be incorporated into more general optimisation techniques, allowing the optimisation of both the parameters for a lensing model and the surface brightness of the source.

9 pages, to appear in PASA

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• Gravitational Lensing in Astronomy
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• LensClean revisited
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• Strong Gravitational Lens Inversion: A Bayesian Approach
• Lensview: Software for modelling resolved gravitational lens images
• A Systematic Review of Strong Gravitational Lens Modeling Software
• Extended lens reconstructions with Grale: exploiting time domain, substructural and weak-lensing information
• Model-independent and model-based local lensing properties of CL0024+1654 from multiply-imaged galaxies
• The Einstein Ring 0047-2808 Revisited: A Bayesian Inversion
• Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers
• Cosmological parameter constraints using phenomenological symbolic expressions: On the significance of symbolic expression complexity and accuracy
• Applications of Bayesian Probability Theory in Astrophysics