A Bayesian Analysis of Regularised Source Inversions in Gravitational Lensing

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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 ... continued below

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16 pages

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Suyu, Sherry H.; Marshall, P. J.; Hobson, M. P. & Blandford, R. D. January 25, 2006.

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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.

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16 pages

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  • Report No.: SLAC-PUB-11658
  • Grant Number: AC02-76SF00515
  • DOI: 10.2172/875811 | External Link
  • Office of Scientific & Technical Information Report Number: 875811
  • Archival Resource Key: ark:/67531/metadc877649

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Creation Date

  • January 25, 2006

Added to The UNT Digital Library

  • Sept. 21, 2016, 2:29 a.m.

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  • Aug. 1, 2017, 12:01 p.m.

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Suyu, Sherry H.; Marshall, P. J.; Hobson, M. P. & Blandford, R. D. A Bayesian Analysis of Regularised Source Inversions in Gravitational Lensing, report, January 25, 2006; [Menlo Park, California]. (digital.library.unt.edu/ark:/67531/metadc877649/: accessed September 25, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.