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 Gaussiandistributed 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 Gaussiandistributed 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 highperformance computing resources for application to ongoing wide field lensing surveys.
 Authors:
 Lawrence Livermore National Laboratory, Livermore, CA 94551 (United States)
 University of California, Davis, Davis, CA 95616 (United States)
 Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94035 (United States)
 Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States)
 National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 947208150 (United States)
 Publication Date:
 OSTI Identifier:
 22661160
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Astrophysical Journal; Journal Volume: 839; Journal Issue: 1; Other Information: Country of input: International Atomic Energy Agency (IAEA)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; CATALOGS; CONVERGENCE; COSMOLOGY; DATA ANALYSIS; GALAXIES; GALAXY CLUSTERS; GRAVITATIONAL LENSES; MASS; MASS DISTRIBUTION; NOISE; PERFORMANCE; PROBABILISTIC ESTIMATION; RESOURCES; SIMULATION
Citation Formats
Schneider, M. D., Dawson, W. A., Ng, K. Y., Marshall, P. J., Meyers, J. E., and Bard, D. J., Email: schneider42@llnl.gov, Email: dstn@cmu.edu, Email: boutigny@in2p3.fr, Email: djbard@slac.stanford.edu, Email: jmeyers314@stanford.edu. Probabilistic Cosmological Mass Mapping from Weak Lensing Shear. United States: N. p., 2017.
Web. doi:10.3847/15384357/839/1/25.
Schneider, M. D., Dawson, W. A., Ng, K. Y., Marshall, P. J., Meyers, J. E., & Bard, D. J., Email: schneider42@llnl.gov, Email: dstn@cmu.edu, Email: boutigny@in2p3.fr, Email: djbard@slac.stanford.edu, Email: jmeyers314@stanford.edu. Probabilistic Cosmological Mass Mapping from Weak Lensing Shear. United States. doi:10.3847/15384357/839/1/25.
Schneider, M. D., Dawson, W. A., Ng, K. Y., Marshall, P. J., Meyers, J. E., and Bard, D. J., Email: schneider42@llnl.gov, Email: dstn@cmu.edu, Email: boutigny@in2p3.fr, Email: djbard@slac.stanford.edu, Email: jmeyers314@stanford.edu. Mon .
"Probabilistic Cosmological Mass Mapping from Weak Lensing Shear". United States.
doi:10.3847/15384357/839/1/25.
@article{osti_22661160,
title = {Probabilistic Cosmological Mass Mapping from Weak Lensing Shear},
author = {Schneider, M. D. and Dawson, W. A. and Ng, K. Y. and Marshall, P. J. and Meyers, J. E. and Bard, D. J., Email: schneider42@llnl.gov, Email: dstn@cmu.edu, Email: boutigny@in2p3.fr, Email: djbard@slac.stanford.edu, Email: jmeyers314@stanford.edu},
abstractNote = {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 Gaussiandistributed 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 Gaussiandistributed 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 highperformance computing resources for application to ongoing wide field lensing surveys.},
doi = {10.3847/15384357/839/1/25},
journal = {Astrophysical Journal},
number = 1,
volume = 839,
place = {United States},
year = {Mon Apr 10 00:00:00 EDT 2017},
month = {Mon Apr 10 00:00:00 EDT 2017}
}

Here, 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 Gaussiandistributed 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 ormore »

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