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Trans-Dimensional Bayesian Inference for Gravitational Lens Substructures | | Brendon J. Brewer
; David Huijser
; Geraint F. Lewis
; | | Date: |
4 Aug 2015 | | Abstract: | We introduce a Bayesian solution to the problem of inferring the density
profile of strong gravitational lenses when the lens galaxy may contain
multiple dark or faint substructures. The source and lens models are based on a
superposition of an unknown number of non-negative basis functions (or "blobs")
whose form was chosen with speed as a primary criterion. The prior distribution
for the blobs’ properties is specified hierarchically, so the mass function of
substructures is a natural output of the method. We use reversible jump Markov
Chain Monte Carlo (MCMC) within Diffusive Nested Sampling (DNS) to sample the
posterior distribution and evaluate the marginal likelihood of the model,
including the summation over the unknown number of blobs in the source and the
lens. We demonstrate the method on a simulated data set with a single
substructure, which is recovered well with moderate uncertainties. We also
apply the method to the g-band image of the "Cosmic Horseshoe" system, and find
some hints of potential substructures. However, we caution that such results
could also be caused by misspecifications in the model (such as the shape of
the smooth lens component or the point spread function), which are difficult to
guard against in full generality. | | Source: | arXiv, 1508.0662 | | Services: | Forum | Review | PDF | Favorites | |
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