Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: Application to DES SV

DES Collaboration

doi:10.1093/mnras/sty1252

Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: Application to DES SV

DES Collaboration

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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
Pages (from-to)	2871-2888
Number of pages	18
Journal	Monthly Notices of the Royal Astronomical Society
Volume	479
Issue number	3
DOIs	https://doi.org/10.1093/mnras/sty1252
State	Published - Sep 21 2018

Funding

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). 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. DJJ acknowledges the support of the National Science Foundation, award AST-1440254. 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 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. 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.

Funders	Funder number
Australian Research Council Centre of Excellence for All-sky Astrophysics
CAAS-TRO	CE110001020
Collaborating Institutions in the Dark Energy Survey
Conselho Nacional de Desen-volvimento Científico e Tecnológico
European Community
European Union’s Seventh Framework Program
FP7/2007
Fermi Research Alliance, LLC	DE-AC02-07CH11359
H2020 Framework Program of the European Commission
INCT
Kavli Institute of Cosmological Physics at the University of Chicago
Ministry of Science and Education of Spain
Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University
National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
U.S. National Science Foundation
UK Science and Technology Research Council
National Science Foundation	1138766, AST-1138766, 1125897, AST-1440254, AST-1536171
U.S. Department of Energy
Office of Science
High Energy Physics
Horizon 2020 Framework Programme	665044
Seventh Framework Programme	240672, 306478, 291329
Higher Education Funding Council for England
Center for Cosmology and Astroparticle Physics, Ohio State University
Instituto Nacional de Ciência e Tecnologia Midas
Science and Technology Facilities Council	ST/M001334/1
Royal Society
European Commission
European Research Council	FP7/291329
Deutsche Forschungsgemeinschaft
Generalitat de Catalunya
Ministerio de Economía y Competitividad	SEV-2016-0588, SEV-2016-0597, ESP2015-66861, MDM-2015-0509, FPA2015-68048, AYA2015-71825
Conselho Nacional de Desenvolvimento Científico e Tecnológico	465376/2014-2
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Financiadora de Estudos e Projetos
Ministério da Ciência e Tecnologia
European Regional Development Fund
National Science Foundation

Keywords

Gravitational lensing: weak
Large-scale structure of universe
Methods: statistical

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10.1093/mnras/sty1252

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@article{b15b508b664a4d15a4d0334bbb45c9df,

title = "Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: Application to DES SV",

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.",

keywords = "Gravitational lensing: weak, Large-scale structure of universe, Methods: statistical",

author = "{DES Collaboration} and N. Jeffrey and Abdalla, {F. B.} and O. Lahav and F. Lanusse and Starck, {L. J.} and A. Leonard and D. Kirk and C. Chang and E. Baxter and T. Kacprzak and S. Seitz and V. Vikram and L. Whiteway and Abbott, {T. M.C.} and S. Allam and S. Avila and E. Bertin and D. Brooks and {Carnero Rosell}, A. and {Carrasco Kind}, M. and J. Carretero and Castander, {F. J.} and M. Crocce and Cunha, {C. E.} and D'Andrea, {C. B.} and {da Costa}, {L. N.} and C. Davis and {De Vicente}, J. and S. Desai and P. Doel and Eifler, {T. F.} and Evrard, {A. E.} and B. Flaugher and P. Fosalba and J. Frieman and J. Garc{\'i}a-Bellido and Gerdes, {D. W.} and D. Gruen and Gruendl, {R. A.} and J. Gschwend and G. Gutierrez and Hartley, {W. G.} and K. Honscheid and B. Hoyle and James, {D. J.} and M. Jarvis and K. Kuehn and M. Lima and H. Lin and E. Suchyta",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2018. Published by Oxford University Press on behalf of The Royal Astronomical Society.",

year = "2018",

month = sep,

day = "21",

doi = "10.1093/mnras/sty1252",

language = "English",

volume = "479",

pages = "2871--2888",

journal = "Monthly Notices of the Royal Astronomical Society",

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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 - Suchyta, E.

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

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U2 - 10.1093/mnras/sty1252

DO - 10.1093/mnras/sty1252

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SN - 0035-8711

VL - 479

SP - 2871

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JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 3

ER -

Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: Application to DES SV

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