Speaker
Jozef Bucko
(ETH Zurich)
Description
Progress in computing and machine learning has enabled an efficient extraction of information from cosmological fields beyond the Gaussian regime. In our work, we investigate the potential of combining non-Gaussian information from weak lensing and galaxy clustering observations to improve constraints on cosmological parameters. We develop a forward model based on the CosmoGrid simulation suite, allowing us to generate up to 1'000'000 independent simulated survey maps. We combine lensing and clustering statistics for a stage III-like survey and showcase the gain of reaching beyond 2-point statistics.
Author
Jozef Bucko
(ETH Zurich)