Weak lensing mass reconstruction using sparsity and a Gaussian random field

Understand

We introduce a novel approach to reconstruct dark matter mass maps from weak gravitational lensing measurements.

• The cornerstone of the proposed method lies in a new modelling of the matter density field in the Universe as a mixture of two components:(1) a sparsity-based component that captures the non-Gaussian structure of the field, such as peaks or halos at different spatial scales; and (2) a Gaussian random field, which is known to well represent the linear characteristics of the field.Methods.
• We propose an algorithm called MCALens which jointly estimates these two components.
• MCAlens is based on an alternating minimization incorporating both sparse recovery and a proximal iterative Wiener filtering.