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- W2904278998 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 45% on top of the power spectrum, making it a powerful source of information for future surveys. Interestingly, the super sample covariance has a negligible impact on this joint analysis. 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 problem." @default.
- W2904278998 created "2018-12-22" @default.
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- W2904278998 date "2019-01-09" @default.
- W2904278998 modified "2023-09-23" @default.
- W2904278998 title "Information content of the weak lensing bispectrum for the next generation of galaxy surveys" @default.
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