Skip to content
Back to outputs

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

Research output: Contribution to journalArticle

Standard

Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV. / Dark Energy Survey Collaboration; Avila Perez, Santiago Javier; Thomas, Daniel.

In: Monthly Notices of the Royal Astronomical Society, Vol. 479, No. 3, sty1252, 21.09.2018, p. 2871-2888.

Research output: Contribution to journalArticle

Harvard

Dark Energy Survey Collaboration, Avila Perez, SJ & Thomas, D 2018, 'Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV', Monthly Notices of the Royal Astronomical Society, vol. 479, no. 3, sty1252, pp. 2871-2888. https://doi.org/10.1093/mnras/sty1252

APA

Dark Energy Survey Collaboration, Avila Perez, S. J., & Thomas, D. (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. [sty1252]. https://doi.org/10.1093/mnras/sty1252

Vancouver

Dark Energy Survey Collaboration, Avila Perez SJ, Thomas D. Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV. Monthly Notices of the Royal Astronomical Society. 2018 Sep 21;479(3):2871-2888. sty1252. https://doi.org/10.1093/mnras/sty1252

Author

Dark Energy Survey Collaboration ; Avila Perez, Santiago Javier ; Thomas, Daniel. / Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV. In: Monthly Notices of the Royal Astronomical Society. 2018 ; Vol. 479, No. 3. pp. 2871-2888.

Bibtex

@article{c7a63949e56d4ad488fbca81bd5803f9,
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. 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 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 KS with a range of metrics. Both the Wiener filter and GLIMPSE convergence reconstructions show a 12% improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods{\textquoteright} abilities to find mass peaks, we measure the difference between peak counts from simulated ΛCDM 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{\textquoteright} concentration is improved 17% by GLIMPSE and 18% by the Wiener filter. The correlation between reconstructions from data and foreground redMaPPer clusters is increased 18% by the Wiener filter and 32% by GLIMPSE.",
keywords = "RCUK, STFC, ST/M001334/1, AST-1138766, AST-1536171, AST-1440254, gravitational lensing: weak, methods: statistical, large-scale structure of Universe",
author = "{Dark Energy Survey Collaboration} and {Avila Perez}, {Santiago Javier} and Daniel Thomas",
year = "2018",
month = sep,
day = "21",
doi = "10.1093/mnras/sty1252",
language = "English",
volume = "479",
pages = "2871--2888",
journal = "MNRAS",
issn = "0035-8711",
publisher = "Oxford University Press",
number = "3",

}

RIS

TY - JOUR

T1 - Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV

AU - Dark Energy Survey Collaboration

AU - Avila Perez, Santiago Javier

AU - Thomas, Daniel

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. 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 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 KS with a range of metrics. Both the Wiener filter and GLIMPSE convergence reconstructions show a 12% 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 ΛCDM 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% by GLIMPSE and 18% by the Wiener filter. The correlation between reconstructions from data and foreground redMaPPer clusters is increased 18% by the Wiener filter and 32% 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. 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 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 KS with a range of metrics. Both the Wiener filter and GLIMPSE convergence reconstructions show a 12% 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 ΛCDM 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% by GLIMPSE and 18% by the Wiener filter. The correlation between reconstructions from data and foreground redMaPPer clusters is increased 18% by the Wiener filter and 32% by GLIMPSE.

KW - RCUK

KW - STFC

KW - ST/M001334/1

KW - AST-1138766

KW - AST-1536171

KW - AST-1440254

KW - gravitational lensing: weak

KW - methods: statistical

KW - large-scale structure of Universe

U2 - 10.1093/mnras/sty1252

DO - 10.1093/mnras/sty1252

M3 - Article

VL - 479

SP - 2871

EP - 2888

JO - MNRAS

JF - MNRAS

SN - 0035-8711

IS - 3

M1 - sty1252

ER -

ID: 10761479