publication . Article . Conference object . Other literature type . Preprint . 2018

Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV

A. A. Plazas; A. Roodman; A. Roodman; M. Carrasco Kind; M. Carrasco Kind; Martin Crocce; Joshua A. Frieman; Joshua A. Frieman; Carlos E. Cunha; G. Tarle; ...
Open Access English
  • Published: 01 Sep 2018
  • Publisher: OXFORD UNIV PRESS
Abstract
Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three methods: Kaiser-Squires (KS), Wiener filter, and GLIMPSE. KS is a direct inversion, not accounting for survey masks or noise. The Wiener filter is well-motivated for Gaussian density fields in a Bayesian framework. GLIMPSE uses sparsity, aiming to reconstruct non-linearities in the density field. We compare these methods with several te...
Subjects
arXiv: Astrophysics::Cosmology and Extragalactic AstrophysicsAstrophysics::Galaxy Astrophysics
free text keywords: gravitational lensing: weak, methods: statistical, large-scale structure of Universe, large-scale structure of Universe, methods: statistical, gravitational lensing: weak, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Lentes gravitacionais, Estruturas em larga escala (Astronomia), Galáxias, Métodos estatísticos, Gravitational lenses, Large scale structure (Astronomy), Galaxies, Statistical methods, Gravitational lensing - Weak, Large scale structure of universe, [ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph], Space and Planetary Science, Astronomy and Astrophysics, weak; methods: statistical; large-scale structure of Universe [gravitational lensing], RCUK, STFC, ST/M001334/1, AST-1138766, AST-1536171, AST-1440254, weak [gravitational lensing], statistical [methods], Astrophysics - Cosmology and Nongalactic Astrophysics, Pearson product-moment correlation coefficient, symbols.namesake, symbols, Cosmology, Gaussian, Physics, Prior probability, Statistical physics, Wiener filter, Dark matter, Dark energy, Weak gravitational lensing
Funded by
EC| DEDALE
Project
DEDALE
Data Learning on Manifolds and Future Challenges
  • Funder: European Commission (EC)
  • Project Code: 665044
  • Funding stream: H2020 | RIA
,
NSF| The Event Horizon Telescope Experiment
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1440254
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
,
NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
,
EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
,
RCUK| UCL Astrophysics Consolidated Grant 2015-2018
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: ST/M001334/1
  • Funding stream: STFC
Communities
FET H2020FET OPEN: FET-Open research projects
FET H2020FET OPEN: Data Learning on Manifolds and Future Challenges
91 references, page 1 of 7

Alsing J., Heavens A., Jaffe A. H., Kiessling A., Wandelt B., Hoffmann T., 2016, MNRAS, 455, 4452

Alsing J., Heavens A., Jaffe A. H., 2017, MNRAS, 466, 3272

Amendola L. et al., 2016, Living Rev. Relativ., 21, 2

Bacon D. J., Goldberg D. M., Rowe B. T. P., Taylor A. N., 2006, MNRAS, 365, 414

Bartelmann M., Schneider P., 2001, Phys. Rep., 340, 291

Becker M. R., 2013, MNRAS, 435, 115

Ben´ıtez N., 2000, ApJ, 536, 571

Bo¨hm V., Hilbert S., Greiner M., Enßlin T. A., 2017, Phys. Rev. D, 96, 123510

Bonnett C. et al., 2016, Phys. Rev. D, 94, 042005

Busha M. T., Wechsler R. H., Becker M. R., Erickson B., Evrard A. E., 2013, in American Astronomical Society Meeting Abstracts #221, The American Astrophysical Society, Washington, p. 341.07

Byrd R. H., Lu P., Nocedal J., Zhu C., 1995, SIAM J. Sci. Comput., 16, 1190

Chang C. et al., 2015, Phys. Rev. Lett., 115, 051301

Chang C. et al., 2016, MNRAS, 459, 3203

Chang C. et al., 2017, MNRAS, 475, 3165

Chapman E. et al., 2013, MNRAS, 429, 165

91 references, page 1 of 7
Abstract
Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three methods: Kaiser-Squires (KS), Wiener filter, and GLIMPSE. KS is a direct inversion, not accounting for survey masks or noise. The Wiener filter is well-motivated for Gaussian density fields in a Bayesian framework. GLIMPSE uses sparsity, aiming to reconstruct non-linearities in the density field. We compare these methods with several te...
Subjects
arXiv: Astrophysics::Cosmology and Extragalactic AstrophysicsAstrophysics::Galaxy Astrophysics
free text keywords: gravitational lensing: weak, methods: statistical, large-scale structure of Universe, large-scale structure of Universe, methods: statistical, gravitational lensing: weak, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Lentes gravitacionais, Estruturas em larga escala (Astronomia), Galáxias, Métodos estatísticos, Gravitational lenses, Large scale structure (Astronomy), Galaxies, Statistical methods, Gravitational lensing - Weak, Large scale structure of universe, [ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph], Space and Planetary Science, Astronomy and Astrophysics, weak; methods: statistical; large-scale structure of Universe [gravitational lensing], RCUK, STFC, ST/M001334/1, AST-1138766, AST-1536171, AST-1440254, weak [gravitational lensing], statistical [methods], Astrophysics - Cosmology and Nongalactic Astrophysics, Pearson product-moment correlation coefficient, symbols.namesake, symbols, Cosmology, Gaussian, Physics, Prior probability, Statistical physics, Wiener filter, Dark matter, Dark energy, Weak gravitational lensing
Funded by
EC| DEDALE
Project
DEDALE
Data Learning on Manifolds and Future Challenges
  • Funder: European Commission (EC)
  • Project Code: 665044
  • Funding stream: H2020 | RIA
,
NSF| The Event Horizon Telescope Experiment
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1440254
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
,
NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
,
EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
,
RCUK| UCL Astrophysics Consolidated Grant 2015-2018
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: ST/M001334/1
  • Funding stream: STFC
Communities
FET H2020FET OPEN: FET-Open research projects
FET H2020FET OPEN: Data Learning on Manifolds and Future Challenges
91 references, page 1 of 7

Alsing J., Heavens A., Jaffe A. H., Kiessling A., Wandelt B., Hoffmann T., 2016, MNRAS, 455, 4452

Alsing J., Heavens A., Jaffe A. H., 2017, MNRAS, 466, 3272

Amendola L. et al., 2016, Living Rev. Relativ., 21, 2

Bacon D. J., Goldberg D. M., Rowe B. T. P., Taylor A. N., 2006, MNRAS, 365, 414

Bartelmann M., Schneider P., 2001, Phys. Rep., 340, 291

Becker M. R., 2013, MNRAS, 435, 115

Ben´ıtez N., 2000, ApJ, 536, 571

Bo¨hm V., Hilbert S., Greiner M., Enßlin T. A., 2017, Phys. Rev. D, 96, 123510

Bonnett C. et al., 2016, Phys. Rev. D, 94, 042005

Busha M. T., Wechsler R. H., Becker M. R., Erickson B., Evrard A. E., 2013, in American Astronomical Society Meeting Abstracts #221, The American Astrophysical Society, Washington, p. 341.07

Byrd R. H., Lu P., Nocedal J., Zhu C., 1995, SIAM J. Sci. Comput., 16, 1190

Chang C. et al., 2015, Phys. Rev. Lett., 115, 051301

Chang C. et al., 2016, MNRAS, 459, 3203

Chang C. et al., 2017, MNRAS, 475, 3165

Chapman E. et al., 2013, MNRAS, 429, 165

91 references, page 1 of 7
Any information missing or wrong?Report an Issue