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

Niall Jeffrey ,D Kirk ,R Miquel ,L Whiteway ,Joshua A Frieman ,T Kacprzak ,C L Chang ,C Davis ,Adrienne Leonard ,P Doel ,François Lanusse ,A Roodman ,T M C Abbott ,G Gutiérrez ,C B D’andrea ,W G Hartley ,T F Eifler ,R A Gruendl ,J Carretero ,M Schubnell ,Daniel Thomas ,C E Cunha ,S Allam ,M March ,Huan Lin ,M Smith ,P Fosalba ,K Reil ,E Bertin ,F J Castander ,Eric J Baxter ,A A Plazas ,L N Da Costa ,K Kuêhn ,A Carnero Rosell ,K Honscheid ,D W Gerdes ,F Sobreira ,V Scarpine ,B Hoyle ,O Lahav ,S Seitz ,G Tarlé ,S Ávila ,S Desai ,M Crocce ,E Suchyta ,F B Abdalla ,E Sánchez ,D J James ,D Brooks ,M Carrasco Kind ,M Soares-Santos ,M E C Swanson ,F Menanteau ,P Melchior ,J L Starck ,I Sevilla-Noarbe ,August E Evrard ,M Lima ,Mike Jarvis ,B Flaugher ,D Gruen ,J García-Bellido ,J Gschwend ,J De Vicente ,V Vikram ,A R Walker

doi:10.1093/mnras/sty1252

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 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 {\Lambda}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.

Highlights

Mass map reconstruction from weak gravitational lensing recovers the underlying matter distribution in the Universe from measurements of galaxy shapes
To ensure that the mass map tests are consistent with different output formats, all maps were converted on to a spherical pixelization using HEALPIX (Gorski et al 2005)
A HEALPIX map comprises 12 subdivisions on the sphere, which are each partitioned into NSIDE × NSIDE grids

Summary

Introduction

Mass map reconstruction from weak gravitational lensing recovers the underlying matter distribution in the Universe from measurements of galaxy shapes. The Dark Energy Survey (DES) has used the 2-point correlation function of shear to contribute to excellent constraints on cosmological parameters and models, including the nature of dark energy (DES Collaboration et al 2017). Shear 2-point correlation functions have been used to constrain cosmology from many other survey data sets (Kilbinger et al 2013; van Uitert et al 2017). These methods use the shear measurements directly, as the shear can be related to the underlying matter distribution without needing to explicitly reconstruct mass maps

Methods

Results

Conclusion