2010MNRAS.401.2257M

The problem of detecting dark matter filaments in the cosmic web is considered. Weak lensing is an ideal probe of dark matter, and therefore forms the basis of particularly promising detection methods. We consider and develop a number of weak lensing techniques that could be used to detect filaments in individual or stacked cluster fields, and apply them to synthetic lensing data sets in the fields of clusters from the Millennium Simulation. These techniques are multipole moments of the shear and convergence, mass reconstruction and parametrized fits to filament mass profiles using a Markov chain Monte Carlo approach. In particular, two new filament detection techniques are explored (multipole shear filters and Markov chain Monte Carlo mass profile fits), and we outline the quality of data required to be able to identify and quantify filament profiles. We also consider the effects of large-scale structure on filament detection. We conclude that using these techniques, there will be realistic prospects of detecting filaments in data from future space-based missions. The methods presented in this paper will be of great use in the identification of dark matter filaments in future surveys.