http://arxiv.org/abs/2105.14699
We present $\texttt{KaRMMa}$, 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 $\texttt{KaRMMa}$ on a suite of dark matter N-body simulations with simulated DES Y1-like shear observations. We show that $\texttt{KaRMMa}$ 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 $\texttt{KaRMMa}$ maps are nearly unbiased in their one- and two-point statistics. In particular, $\texttt{KaRMMa}$ is successful at capturing the non-Gaussian nature of the distribution of $\kappa$ values in the simulated maps.
P. Fiedorowicz, E. Rozo, S. Boruah, et. al.
Tue, 1 Jun 21
46/72
Comments: 10 pages, 9 figures
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