KaRMMa – kappa reconstruction for mass mapping

Author:

Fiedorowicz Pier1ORCID,Rozo Eduardo1,Boruah Supranta S2,Chang Chihway34ORCID,Gatti Marco5

Affiliation:

1. Department of Physics, University of Arizona , Tucson, AZ 85721, USA

2. Steward Observatory, University of Arizona , Tucson, AZ 85719, USA

3. Kavli Institute for Cosmological Physics, University of Chicago , Chicago, IL 60637, USA

4. Department of Astronomy and Astrophysics, University of Chicago , Chicago, IL 60637, USA

5. Department of Physics and Astronomy, University of Pennsylvania , Philadelphia, PA 19104, USA

Abstract

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.

Funder

NSF

DOE

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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