Javier Carrón Duque

A main assumption of our understanding of the Universe is the Cosmological Principle (the Universe is homogeneous and isotropic). However, there is a growing number of observables with apparent dipoles, as well as mildly—significant anomalies in the CMB. I am interested in the study of these effects to assess the validity of the Cosmological Principle.

Deviations from Gaussianity and Isotropy in the cosmological observables can provide useful information not available in the 2pt correlation function, both in the Early and Late Universe. I am interested in the mathematical development and application of non—Gaussianity tools in Cosmology, such as Minkowski Functionals.

The accurate location and analysis of the largest structures of the Universe (such as Cosmic Filaments and Voids) can help us understand the non—linear evolution of these objects. I am interested in their study and interaction with other observables such as Weak Gravitational Lensing.

The application of Machine Learning in Cosmology can help us extract more information from a growing number of datasets, which can complement traditional tools and unveil complex effects. I am interested in identifying cases where Machine Learning can be useful in Cosmology and its implementation, especially with explainable tools.