Probabilistic Cosmological Mass Mapping from Weak Lensing Shear

TL;DR

This paper introduces a probabilistic method for reconstructing cosmological mass maps from weak lensing data, capable of handling complex noise models and scalable to large surveys using high-performance computing.

Contribution

It presents a novel Bayesian framework for mass mapping from galaxy ellipticities that makes no assumptions about shear noise distributions and includes a way to infer cosmological parameters.

Findings

Successfully reconstructed the mass distribution of Abell 781

Demonstrated the algorithm with simulated Gaussian lensing maps

Outlined the computational requirements for large-scale surveys

Abstract

We infer gravitational lensing shear and convergence fields from galaxy ellipticity catalogs under a spatial process prior for the lensing potential. We demonstrate the performance of our algorithm with simulated Gaussian-distributed cosmological lensing shear maps and a reconstruction of the mass distribution of the merging galaxy cluster Abell 781 using galaxy ellipticities measured with the Deep Lens Survey. Given interim posterior samples of lensing shear or convergence fields on the sky, we describe an algorithm to infer cosmological parameters via lens field marginalization. In the most general formulation of our algorithm we make no assumptions about weak shear or Gaussian distributed shape noise or shears. Because we require solutions and matrix determinants of a linear system of dimension that scales with the number of galaxies, we expect our algorithm to require parallel high-performance computing resources for application to ongoing wide field lensing surveys.