Speaker
Description
Next-generation galaxy surveys will provide vast datasets that can significantly improve our understanding of the Universe, provided that we fully exploit their cosmological information content. However, the late-time matter distribution is intrinsically non-Gaussian, limiting the information captured by standard two-point statistics. To recover this information, it is essential to incorporate beyond two-point observables, such as one-point probability density functions (PDF). One-point statistics capture key non-Gaussian features of galaxy clustering, while their combination with weak lensing provides a powerful framework to jointly constrain galaxy bias and cosmology. In particular, the weak lensing convergence PDF is sensitive to the physics of the dark Universe, including dark matter and dark energy. At the same time, robust cosmological inference requires an accurate treatment of systematic effects, such as shape noise, intrinsic alignments, photometric redshift uncertainties, and mass-mapping reconstruction. I will present our efforts to model the joint PDF while incorporating these systematic effects, and to improve cosmological constraints by combining the joint PDF with the standard 3x2-point correlation functions.