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

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

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. 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 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 the Wiener 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 the Wiener 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.

Gravitational lensing: weak, Large-scale structure of universe, Methods: statistical
0035-8711
2871-2888
Jeffrey, N.
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Abdalla, F.B.
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Lahav, O.
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Lanusse, F.
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Leonard, A.
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Kirk, D.
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Chang, C.
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Baxter, E.
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Kacprzak, T.
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Seitz, S.
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Bertin, E.
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Brooks, D.
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Desai, S.
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Eifler, T.F.
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Evrard, A.E.
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Flaugher, B.
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Fosalba, P.
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Frieman, J.
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García-Bellido, J.
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Gerdes, D.W.
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Gruen, D.
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Gruendl, R.A.
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Gschwend, J.
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Gutierrez, G.
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Hartley, W.G.
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Honscheid, K.
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Hoyle, B.
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James, D.J.
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Jarvis, M.
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Kuehn, K.
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Lima, M.
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Lin, H.
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Smith, M.
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DES Collaboration
Jeffrey, N.
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Abdalla, F.B.
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Lahav, O.
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Lanusse, F.
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Leonard, A.
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Kirk, D.
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Chang, C.
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Baxter, E.
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Kacprzak, T.
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Seitz, S.
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Vikram, V.
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Whiteway, L.
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Abbott, T.M.C.
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Allam, S.
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Avila, S.
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Bertin, E.
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Brooks, D.
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Carnero Rosell, A.
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Carrasco Kind, M.
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Carretero, J.
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Castander, F.J.
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Crocce, M.
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Cunha, C.E.
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D'Andrea, C.B.
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da Costa, L.N.
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Davis, C.
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De Vicente, J.
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Desai, S.
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Eifler, T.F.
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Evrard, A.E.
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Flaugher, B.
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Fosalba, P.
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Frieman, J.
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García-Bellido, J.
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Gerdes, D.W.
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Gruen, D.
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Gruendl, R.A.
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Gschwend, J.
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Gutierrez, G.
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Hartley, W.G.
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Honscheid, K.
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Hoyle, B.
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James, D.J.
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Jarvis, M.
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Kuehn, K.
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Lima, M.
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Lin, H.
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Smith, M.
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Jeffrey, N., Abdalla, F.B., Lahav, O., Lanusse, F., Starck, L.J., Leonard, A., Kirk, D., Chang, C., Baxter, E., Kacprzak, T., Seitz, S., Vikram, V., Whiteway, L., Abbott, T.M.C., Allam, S., Avila, S., Bertin, E., Brooks, D., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Castander, F.J., Crocce, M., Cunha, C.E., D'Andrea, C.B., da Costa, L.N., Davis, C., De Vicente, J., Desai, S., Doel, P., Eifler, T.F., Evrard, A.E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D.W., Gruen, D., Gruendl, R.A., Gschwend, J., Gutierrez, G., Hartley, W.G., Honscheid, K., Hoyle, B., James, D.J., Jarvis, M., Kuehn, K., Lima, M., Lin, H. and Smith, M. , DES Collaboration (2018) Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: Application to DES SV. Monthly Notices of the Royal Astronomical Society, 479 (3), 2871-2888. (doi:10.1093/mnras/sty1252).

Record type: Article

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. 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 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 the Wiener 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 the Wiener 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.

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More information

Accepted/In Press date: 10 May 2018
e-pub ahead of print date: 15 May 2018
Published date: 21 September 2018
Keywords: Gravitational lensing: weak, Large-scale structure of universe, Methods: statistical

Identifiers

Local EPrints ID: 423346
URI: https://eprints.soton.ac.uk/id/eprint/423346
ISSN: 0035-8711
PURE UUID: a3e3e684-61e0-4c4f-8e22-88f17e7ce4a4
ORCID for M. Smith: ORCID iD orcid.org/0000-0002-3321-1432

Catalogue record

Date deposited: 20 Sep 2018 16:30
Last modified: 15 Aug 2019 00:33

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Contributors

Author: N. Jeffrey
Author: F.B. Abdalla
Author: O. Lahav
Author: F. Lanusse
Author: L.J. Starck
Author: A. Leonard
Author: D. Kirk
Author: C. Chang
Author: E. Baxter
Author: T. Kacprzak
Author: S. Seitz
Author: V. Vikram
Author: L. Whiteway
Author: T.M.C. Abbott
Author: S. Allam
Author: S. Avila
Author: E. Bertin
Author: D. Brooks
Author: A. Carnero Rosell
Author: M. Carrasco Kind
Author: J. Carretero
Author: F.J. Castander
Author: M. Crocce
Author: C.E. Cunha
Author: C.B. D'Andrea
Author: L.N. da Costa
Author: C. Davis
Author: J. De Vicente
Author: S. Desai
Author: P. Doel
Author: T.F. Eifler
Author: A.E. Evrard
Author: B. Flaugher
Author: P. Fosalba
Author: J. Frieman
Author: J. García-Bellido
Author: D.W. Gerdes
Author: D. Gruen
Author: R.A. Gruendl
Author: J. Gschwend
Author: G. Gutierrez
Author: W.G. Hartley
Author: K. Honscheid
Author: B. Hoyle
Author: D.J. James
Author: M. Jarvis
Author: K. Kuehn
Author: M. Lima
Author: H. Lin
Author: M. Smith ORCID iD

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