Gravitational Lensing Mass Mapping with Gaussian Processes
Abstract
We infer gravitational lensing shear and convergence fields from galaxy ellipticity catalogs under a Gaussian 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 orGaussian-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 present computational performance metrics with approximate algorithms that introduce sparsity in the Gaussian Process kernel.
- Publication:
-
American Astronomical Society Meeting Abstracts #231
- Pub Date:
- January 2018
- Bibcode:
- 2018AAS...23123905S