Glimpse

Glimpse is a weak lensing mass-mapping tool relying a robust sparsity-based regularisation scheme to recover high resolution convergence from either gravitational shear alone or from a combination of shear and flexion. Including flexion allows us to supplement the shear on small scales in order to increase the sensitivity to substructures and the overall resolution of the convergence map.

In order to preserve all available small scale information, Glimpse avoids any binning of the irregularly sampled input shear and flexion fields and treats the mass-mapping problem as a general ill-posed inverse problem, regularised using a multi-scale wavelet sparsity prior. The resulting algorithm incorporates redshift, reduced shear, and reduced flexion measurements for individual galaxies and is made highly efficient by the use of fast Fourier estimators. 

The source code for Glimpse is made publicly available and is hosted on Github at https://github.com/CosmoStat/Glimpse 

Test on realistic dark matter distributions

Glimpse was tested on a set of realistic weak lensing simulations corresponding to typical HST/ACS cluster observations and demonstrate our ability to recover substructures with the inclusion of flexion which are lost if only shear information is used. In particular, we can detect substructures at the 15scale well outside of the critical region of the clusters. In addition, flexion also helps to constrain the shape of the central regions of the main dark matter halos. These simulations, along with the reconstructions produced by Glimpse can be found in this archive : flexion_benchmark.tar.lzma.

nbodySimu
Input simulated convergence fields
GlimpseMeanRec
Reconstructed convergence maps using Glimpse. The top row only uses shear information while the bottom row combines shear and flexion to improve the resolution of the recovered maps. Mean of 100 independent noise realizations.

Publications

F. Lanusse, J.-L. Starck, A. Leonard, S. Pires, High Resolution Weak Lensing Mass-Mapping Combining Shear and Flexion, 2016, arXiv:1603.01599