Weak lensing mass reconstruction using sparsity and a Gaussian random field

TL;DR

This paper presents MCALens, a new method for reconstructing dark matter mass maps from weak lensing data by modeling the matter density as a mixture of sparse non-Gaussian features and a Gaussian field, improving accuracy.

Contribution

The paper introduces MCALens, a novel algorithm that jointly models non-Gaussian and Gaussian components of the matter density field for better mass map reconstruction.

Findings

Improved accuracy over existing methods on simulated data

Effective separation of non-Gaussian and Gaussian features

Demonstrates robustness in mass map estimation

Abstract

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. Experimental results on simulated data show that the proposed method exhibits improved estimation accuracy compared to state-of-the-art mass map reconstruction methods.