2017/01/31 by Vanessa Böhm, Stefan Hilbert, Maksim Greiner +2 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Bayesian probability #Cartography #Cosmology and Gravitation Theories #Econometrics #Galaxies: Formation, Evolution, Phenomena #Gaussian Processes and Bayesian Inference #Geography #Log-normal distribution #Mathematics #Optics #Physics #Scale (ratio) #Statistical physics #Statistics #Tomography #astro-ph.CO
paper · pdf · doi:10.1103/physrevd.96.123510
published as Phys. Rev. D 96, 123510 (2017) · 23 pages, 12 figures; updated to match version accepted for publication in PRD
arxiv created 2017/11/20 · openalex publication_date 2017/12/12 · arxiv updated 2017/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 is designed to also work with non-Gaussian posterior distributions which arise, for example, from a non-Gaussian prior distribution. In this work, we use a lognormal prior and compare the reconstruction results to a Gaussian prior 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, but with the lognormal reconstruction being prone to mass-sheet degeneracy. In the low-noise regime and on small scales, the lognormal model produces 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 distribution, and (3) yields a higher pixel-wise correlation between the reconstruction and the true density.