Publication : t18/191

Abstract:
We present a full analysis of the information content in the joint convergence weak lensing power spectrum-bispectrum accounting for the complexity of modern weak lensing large scale structure (LSS) surveys. We developed a high performance code that allows highly parallelized prediction of the binned tomographic observables and their joint non-Gaussian covariance matrix accounting for terms up to the 6-point correlation function and super sample effects. This performance allows us to address several interesting scientific questions. We find that the bispectrum provides an improvement in terms of signal-to-noise ratio (S/N) of about 10% on top of the power spectrum, making it a non negligible source of information for future surveys. Furthermore, we are capable to test the impact of theoretical uncertainties in the halo model used to build our observables; with presently allowed variations we conclude that the impact is negligible on the S/N. Finally, we consider data compression possibilities to optimize future analyses of the weak lensing bispectrum. We find that, ignoring systematics, 5 equipopulated redshift bins are enough to recover the information content of a Euclid-like survey, with negligible improvement when increasing to 10 bins. We also explore principal component analysis and dependence on the triangle shapes as ways to reduce the numerical complexity of the proble