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. Kaiser-Squires is a direct inversion, not accounting for survey masks or noise. TheWiener 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 tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and GLIMPSE offer substantial improvements over smoothed Kaiser-Squires with a range of metrics. Both theWiener filter and GLIMPSE convergence reconstructions show a 12 per cent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods' abilities to find mass peaks, we measure the difference between peak counts from simulated (n-ary logical and)CDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factor of 3.5 for the Wiener filter and 9 for GLIMPSE. With simulations, we measure the reconstruction of the harmonic phases; the phase residuals' concentration is improved 17 per cent by GLIMPSE and 18 per cent by theWiener filter. The correlation between reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by GLIMPSE.
Original language | English (US) |
---|---|
Pages (from-to) | 2871-2888 |
Number of pages | 18 |
Journal | Monthly Notices of the Royal Astronomical Society |
Volume | 479 |
Issue number | 3 |
DOIs | |
State | Published - Sep 21 2018 |
All Science Journal Classification (ASJC) codes
- Astronomy and Astrophysics
- Space and Planetary Science
Keywords
- Gravitational lensing: weak
- Large-scale structure of universe
- Methods: statistical
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In: Monthly Notices of the Royal Astronomical Society, Vol. 479, No. 3, 21.09.2018, p. 2871-2888.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Improving weak lensing mass map reconstructions using Gaussian and sparsity priors
T2 - Application to DES SV
AU - DES Collaboration
AU - Jeffrey, N.
AU - Abdalla, F. B.
AU - Lahav, O.
AU - Lanusse, F.
AU - Starck, L. J.
AU - Leonard, A.
AU - Kirk, D.
AU - Chang, C.
AU - Baxter, E.
AU - Kacprzak, T.
AU - Seitz, S.
AU - Vikram, V.
AU - Whiteway, L.
AU - Abbott, T. M.C.
AU - Allam, S.
AU - Avila, S.
AU - Bertin, E.
AU - Brooks, D.
AU - Carnero Rosell, A.
AU - Carrasco Kind, M.
AU - Carretero, J.
AU - Castander, F. J.
AU - Crocce, M.
AU - Cunha, C. E.
AU - D'Andrea, C. B.
AU - da Costa, L. N.
AU - Davis, C.
AU - De Vicente, J.
AU - Desai, S.
AU - Doel, P.
AU - Eifler, T. F.
AU - Evrard, A. E.
AU - Flaugher, B.
AU - Fosalba, P.
AU - Frieman, J.
AU - García-Bellido, J.
AU - Gerdes, D. W.
AU - Gruen, D.
AU - Gruendl, R. A.
AU - Gschwend, J.
AU - Gutierrez, G.
AU - Hartley, W. G.
AU - Honscheid, K.
AU - Hoyle, B.
AU - James, D. J.
AU - Jarvis, M.
AU - Kuehn, K.
AU - Lima, M.
AU - Lin, H.
AU - Melchior, P.
N1 - Funding Information: The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAAS-TRO), through project number CE110001020, and the Brazilian In-stituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2). Funding Information: NJ, FBA, and J-LS acknowledge support from the European Community through the DEDALE grant (contract no. 665044) within the H2020 Framework Program of the European Commission. OL acknowledges support from a European Research Council Advanced Grant FP7/291329 and support from the UK Science and Technology Research Council (STFC) Grant No. ST/M001334/1. FBA also acknowledges the support of the Royal Society for a University Research Fellowship. Funding Information: DJJ acknowledges the support of the National Science Foundation, award AST-1440254. Funding Information: NJ, FBA, and J-LS acknowledge support from the European Community through theDEDALE grant (contract no. 665044) within the H2020 Framework Program of the European Commission. OL acknowledges support from a European Research Council Advanced Grant FP7/291329 and support from the UK Science and Technology Research Council (STFC) Grant No. ST/M001334/1. FBA also acknowledges the support of the Royal Society for a University Research Fellowship. DJJ acknowledges the support of the National Science Foundation, award AST-1440254. We are grateful for the extraordinary contributions of our CTIO colleagues and the DECam Construction, Commissioning and Science Verification teams in achieving the excellent instrument and telescope conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the DES Data Management group. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, theMinistry of Science and Education of Spain, the Science and Technology FacilitiesCouncil of theUnitedKingdom, theHigher Education Funding Council for England, theNationalCenter for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional deDesenvolvimento Cientifico e Tecnoĺogico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnoĺogicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Fisica d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, TexasA&MUniversity, and theOzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015- 71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV- 2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) includingERCgrant agreements 240672, 291329, and 306478.We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. TheUnited StatesGovernment retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. We use the visualization software package SKYMAPPER4 for the map figures. Funding Information: Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundac¸ão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desen-volvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovac¸ão, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. Funding Information: This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Publisher Copyright: © The Author(s) 2018. Published by Oxford University Press on behalf of The Royal Astronomical Society.
PY - 2018/9/21
Y1 - 2018/9/21
N2 - 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. Kaiser-Squires is a direct inversion, not accounting for survey masks or noise. TheWiener 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 tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and GLIMPSE offer substantial improvements over smoothed Kaiser-Squires with a range of metrics. Both theWiener filter and GLIMPSE convergence reconstructions show a 12 per cent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods' abilities to find mass peaks, we measure the difference between peak counts from simulated (n-ary logical and)CDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factor of 3.5 for the Wiener filter and 9 for GLIMPSE. With simulations, we measure the reconstruction of the harmonic phases; the phase residuals' concentration is improved 17 per cent by GLIMPSE and 18 per cent by theWiener filter. The correlation between reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by GLIMPSE.
AB - 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. Kaiser-Squires is a direct inversion, not accounting for survey masks or noise. TheWiener 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 tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and GLIMPSE offer substantial improvements over smoothed Kaiser-Squires with a range of metrics. Both theWiener filter and GLIMPSE convergence reconstructions show a 12 per cent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods' abilities to find mass peaks, we measure the difference between peak counts from simulated (n-ary logical and)CDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factor of 3.5 for the Wiener filter and 9 for GLIMPSE. With simulations, we measure the reconstruction of the harmonic phases; the phase residuals' concentration is improved 17 per cent by GLIMPSE and 18 per cent by theWiener filter. The correlation between reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by GLIMPSE.
KW - Gravitational lensing: weak
KW - Large-scale structure of universe
KW - Methods: statistical
UR - http://www.scopus.com/inward/record.url?scp=85051511168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051511168&partnerID=8YFLogxK
U2 - 10.1093/mnras/sty1252
DO - 10.1093/mnras/sty1252
M3 - Article
AN - SCOPUS:85051511168
SN - 0035-8711
VL - 479
SP - 2871
EP - 2888
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 3
ER -