Probabilistic Cosmological Mass Mapping from Weak Lensing Shear

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.

22 pages, 17 figures, submitted, comments welcome

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Cited by in corpus (5)

• Merging Cluster Collaboration: A Panchromatic Atlas of Radio Relic Mergers
• Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV
• Probabilistic Mass Mapping with Neural Score Estimation
• The Correspondence between Convergence Peaks from Weak Lensing and Massive Dark Matter Haloes
• The Weak Lensing Peak Statistics in the Mocks by the inverse-Gaussianization Method