PhD project: Dissecting the cosmic web with caustics

Project description

The cosmic web is the largest structural pattern known in nature. Galaxies, gas, and dark matter are woven into a complex weblike structure on scales of a few up to a hundred megaparsec, forming dense compact clusters, elongated filaments, and sheetlike walls surrounding near-empty void regions. The complex connectivity and intrinsic multiscale character of the cosmic web reflect the primordial conditions out of which the structure and variety of objects in the universe have emerged through non-linear gravitational evolution. 

Observations of the cosmic web allow us to infer its underlying physics and the cosmological parameters. Many current methods exploring the cosmic web are based on N-body simulations, which numerically simulate cosmological structure formation. Although they offer a good representation of all aspects of the structure formation process and can help obtain an impression of the cosmic web, these methods are too computationally expensive to explore the variety of possible cosmologies or to do statistical inference on cosmological information, such as galaxy redshift surveys. 

This project will address this issue by developing new methods for galaxy redshift surveys that exploit the geometry and topology of the cosmic web to infer constraints on the underlying physics and cosmological parameters. In particular, we will use Caustic Skeleton theory (Feldbrugge et al. 2019) to connect the cosmic web to simple primordial cosmological conditions (see figure below). This allows us, for a wide variety of cosmologies, to explore cosmic web configurations. We will use the latter as input for a state-of-the-art Machine Learning pipeline to do statistical inference. This project especially focuses on the influence of dark energy, dark matter and neutrino masses on the cosmic web. The pipeline we will develop will be directly applicable to the upcoming redshift surveys including DESI, Euclid and Vera Rubin Observatory.

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