KaRMMa - Kappa reconstruction for mass mapping

Pier Fiedorowicz, Eduardo Rozo, Supranta S. Boruah, Chihway Chang, Marco Gatti

Research output: Contribution to journalArticlepeer-review

Abstract

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 is 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.

Original languageEnglish (US)
Pages (from-to)73-85
Number of pages13
JournalMonthly Notices of the Royal Astronomical Society
Volume512
Issue number1
DOIs
StatePublished - May 1 2022

Keywords

  • dark matter
  • large-scale structure of Universe

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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