Non-Gaussian cosmology: theoretical and statistical challenges for model galaxy surveys
M. Rizzato. PhD Thesis, 26th September 2019, Institut d'Astrophysique de Paris.
Absract: In this thesis, we address key points for an efficient implementation of likelihood codes for modern weak lensing large-scale structure surveys. Specifically, we will focus on the joint weak lensing convergence power spectrum-bispectrum probe and we will tackle the numerical challenges required by realistic analyses. In order to clearly convey the importance of our research, we first provide an in-depth review of the background material required for a comprehensive understanding of the final results. The cosmological context of the study is provided, followed by a description of the technical elements inherent to unbiased covariance matrix estimation for the probe considered. Under the assumption of multivariate Gaussian likelihood, we developed a high performance code that allows highly parallelised prediction of the binned tomographic observables and of 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 qualitatively 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 alone, making it a non-negligible source of information for future surveys. Furthermore, we are capable to address 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 optimise 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.