2021/05/31 by Pier Fiedorowicz, Eduardo Rozo, Supranta S. Boruah +4
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Bayesian probability #Computer science #Galaxies: Formation, Evolution, Phenomena #Gamma-ray bursts and supernovae #Gaussian #Gaussian Processes and Bayesian Inference #Mathematics #Physics #Posterior probability #Statistical physics #Statistics #astro-ph.CO
paper · pdf · doi:10.1093/mnras/stac468
13 pages, 11 figures
arxiv created 2022/01/25 · openalex publication_date 2022/02/17 · openalex created_date 2022/02/24 · arxiv updated 2022/03/02 · openalex updated_date 2026/08/05
We present KaRMMa, a novel method for performing mass map reconstruction from weak-lensing surveys. We employ a fully Bayesian approach with a physically motivated lognormal prior to sample from the posterior distribution of convergence maps. We test KaRMMa on a suite of dark matter N-body simulations with simulated DES Y1-like shear observations. We show that KaRMMa outperforms the basic Kaiser-Squires mass map reconstruction in two key ways: 1) our best map point estimate has lower residuals compared to Kaiser-Squires; and 2) unlike the Kaiser-Squires reconstruction, the posterior distribution of KaRMMa maps are nearly unbiased in all summary statistics we considered, namely: one-point and two-point functions, and peak/void counts. In particular, KaRMMa successfully captures the non-Gaussian nature of the distribution of κ values in the simulated maps. We further demonstrate that the KaRMMa posteriors correctly characterize the uncertainty in all summary statistics we considered.