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Bayesian Analysis of Regularised Source Inversions in Gravitational Lensing.

DE2006875811

Publication Date	2006
Personal Author	Suyu, S. H.; Marshall, P. J.; Hobson, M. P.; Blandford, R. D.
Page Count	11
Abstract	Strong gravitational lens systems with extended sources are of special interest because they provide additional constraints on the models of the lens systems. To use a gravitational lens system for measuring the Hubble constant, one would need to determine the lens potential and the source intensity distribution simultaneously. A linear inversion method to reconstruct a pixellated source distribution of a given lens potential model was introduced by Warren and Dye. In the inversion process, a regularization on the source intensity is often needed to ensure a successful inversion with a faithful resulting source. In this paper, we use Bayesian analysis to determine the optimal regularization constant (strength of regularization) of a given form of regularization and to objectively choose the optimal form of regularization given a selection of regularizations. We consider and compare quantitatively three different forms of regularization previously described in the literature for source inversions in gravitational lensing: zeroth-order, gradient and curvature. We use simulated data with the exact lens potential to demonstrate the method. We find that the preferred form of regularization depends on the nature of the source distribution.
Keywords	Bayesian analysis Red shift Mathematical models Calculation methods Lens systems Linear inversion methods Regularized source inversions Gravitational lenses
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Cambridge Univ. (England). Cavendish Lab.; Stanford Linear Accelerator Center, CA.; Department of Energy, Washington, DC.
Supplemental Notes	Prepared in cooperation with Stanford Linear Accelerator Center, CA. Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200619

Bayesian Analysis of Regularised Source Inversions in Gravitational Lensing.

Bayesian Analysis of Regularised Source Inversions in Gravitational Lensing.
DE2006875811

Publication Date	2006
Personal Author	Suyu, S. H.; Marshall, P. J.; Hobson, M. P.; Blandford, R. D.
Page Count	11
Abstract	Strong gravitational lens systems with extended sources are of special interest because they provide additional constraints on the models of the lens systems. To use a gravitational lens system for measuring the Hubble constant, one would need to determine the lens potential and the source intensity distribution simultaneously. A linear inversion method to reconstruct a pixellated source distribution of a given lens potential model was introduced by Warren and Dye. In the inversion process, a regularization on the source intensity is often needed to ensure a successful inversion with a faithful resulting source. In this paper, we use Bayesian analysis to determine the optimal regularization constant (strength of regularization) of a given form of regularization and to objectively choose the optimal form of regularization given a selection of regularizations. We consider and compare quantitatively three different forms of regularization previously described in the literature for source inversions in gravitational lensing: zeroth-order, gradient and curvature. We use simulated data with the exact lens potential to demonstrate the method. We find that the preferred form of regularization depends on the nature of the source distribution.
Keywords	Bayesian analysis Red shift Mathematical models Calculation methods Lens systems Linear inversion methods Regularized source inversions Gravitational lenses
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Cambridge Univ. (England). Cavendish Lab.; Stanford Linear Accelerator Center, CA.; Department of Energy, Washington, DC.
Supplemental Notes	Prepared in cooperation with Stanford Linear Accelerator Center, CA. Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200619