KaRMMa -- Kappa Reconstruction for Mass Mapping

TL;DR

KaRMMa introduces a Bayesian mass mapping method that outperforms traditional techniques by providing lower residuals and nearly unbiased posterior distributions, effectively capturing non-Gaussian features in weak-lensing data.

Contribution

KaRMMa is a new Bayesian approach with a lognormal prior for mass map reconstruction, improving accuracy and uncertainty quantification over existing methods.

Findings

KaRMMa achieves lower residuals than Kaiser-Squires.

KaRMMa's posterior maps are nearly unbiased across multiple statistics.

It successfully captures non-Gaussian features of the convergence distribution.

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 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 $\kappa$ values in the simulated maps. We further demonstrate that the KaRMMa posteriors correctly characterize the uncertainty in all summary statistics we considered.