Jeffrey, N;
Abdalla, FB;
Lahav, O;
Lanusse, F;
Starck, J-L;
Leonard, A;
Kirk, D;
... DES Collaboration, .; + view all
(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)
pp. 2871-2888.
10.1093/mnras/sty1252.
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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. 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 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 per cent by GLIMPSE and 18 per cent by the Wiener filter. The correlationbetween reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by GLIMPSE.
| Type: | Article |
|---|---|
| Title: | Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: application to DES SV |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/sty1252 |
| Publisher version: | https://doi.org/10.1093/mnras/sty1252 |
| Language: | English |
| Additional information: | © The Author(s) 2018. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | gravitational lensing: weak, methods: statistical, large-scale structure of Universe |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10052504 |
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