Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV
Creators: name: Jeffrey, N.
name: Abdalla, F. B.
name: Lahav, O.
name: Lanusse, F.
name: Starck, J. -L.
name: Leonard, A.
name: Kirk, D.
name: Chang, C.
name: Baxter, E.
name: Kacprzak, T.
name: Seitz, S.
name: Vikram, V.
name: Whiteway, L.
name: Abbott, T. M. C.
name: Allam, S.
name: Avila, S.
name: Bertin, E.
name: Brooks, D.
name: Rosell, A. Carnero
name: Kind, M. Carrasco
name: Carretero, J.
name: Castander, F. J.
name: Crocce, M.
name: Cunha, C. E.
name: D'Andrea, C. B.
name: da Costa, L. N.
name: Davis, C.
name: De Vicente, J.
name: Desai, S.
name: Doel, P.
name: Eifler, T. F.
name: Evrard, A. E.
name: Flaugher, B.
name: Fosalba, P.
name: Frieman, J.
name: Garcia-Bellido, J.
name: Gerdes, D. W.
name: Gruen, D.
name: Gruendl, R. A.
name: Gschwend, J.
name: Gutierrez, G.
name: Hartley, W. G.
name: Honscheid, K.
name: Hoyle, B.
name: James, D. J.
name: Jarvis, M.
name: Kuehn, K.
name: Lima, M.
name: Lin, H.
name: March, M.
name: Melchior, P.
name: Menanteau, F.
name: Miquel, R.
name: Plazas, A. A.
name: Reil, K.
name: Roodman, A.
name: Sanchez, E.
name: Scarpine, V.
name: Schubnell, M.
name: Sevilla-Noarbe, I.
name: Smith, M.
name: Soares-Santos, M.
name: Sobreira, F.
name: Suchyta, E.
name: Swanson, M. E. C.
name: Tarle, G.
name: Thomas, D.
name: Walker, A. R.
Publisher: name: arXiv
Year: 2018
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: 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 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 correlation between reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by GLIMPSE.
description: 19 pages, 10 figures, MNRAS published: 15 May 2018