Persistent homology in cosmic shear: constraining parameters with topological data analysis

Understand

In recent years, cosmic shear has emerged as a powerful tool to study the statistical distribution of matter in our Universe.

• Apart from the standard two-point correlation functions, several alternative methods like peak count statistics offer competitive results.
• Here we show that persistent homology, a tool from topological data analysis, can extract more cosmological information than previous methods from the same dataset.
• For this, we use persistent Betti numbers to efficiently summarise the full topological structure of weak lensing aperture mass maps.