Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior [CEA]

Bayesian and Lognormal! How could I resist a reblog of this arXiver post?

arXiver

http://arxiv.org/abs/1701.01886

We present a Bayesian reconstruction algorithm that infers the three-dimensional large-scale matter distribution from the weak gravitational lensing effects measured in the image shapes of galaxies. The algorithm assumes that the prior probability distribution of the matter density is lognormal, in contrast to many existing methods that assume normal (Gaussian) distributed density fields. We compare the reconstruction results for both priors in a suite of increasingly realistic tests on mock data. We find that in cases of high noise levels (i.e. for low source galaxy densities and/or high shape measurement uncertainties), both normal and lognormal priors lead to reconstructions of comparable quality. In the low-noise regime, however, the lognormal model produces significantly better reconstructions than the normal model: The lognormal model 1) enforces non-negative densities, while negative densities are present when a normal prior is employed, 2) better traces the extremal values and the skewness of the true underlying…

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2 Responses to “Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior [CEA]”

  1. And what about truncated normal priors, if the information on the modeled parameter being positive is certain, normal priors cannot be used anyway!

  2. [32] P. Coles and B. Jones, MNRAS 248, 1 (1991). 🙂

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