On modeling galaxy-scale strong lens systems

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22 Scopus citations

Abstract

I review methods for modeling gravitational lens systems comprising multiple images of a background source surrounding a foreground galaxy. In a Bayesian framework, the likelihood is driven by the nature of the data, which in turn depends on whether the source is point-like or extended. The prior encodes astrophysical expectations about lens galaxy mass distributions, either through a careful choice of model families, or through an explicit Bayesian prior applied to under-constrained free-form models. We can think about different lens modeling methods in terms of their choices of likelihoods and priors.

Original languageEnglish (US)
Pages (from-to)2151-2176
Number of pages26
JournalGeneral Relativity and Gravitation
Volume42
Issue number9
DOIs
StatePublished - 2010

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)

Keywords

  • Galaxy structure
  • Lens modeling
  • Statistical methods
  • Strong gravitational lensing

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