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 intriguing 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.

General Relativity describe the geometry of the Universe locally, but there are many possible manifolds that support a FLRW metric (even in the flat case). The topology of the Universe would have measurable effects on cosmological observables, such as breaking statistical isotropy. I am interested on how to predict and measure these effects.

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.

I am part of the COMPACT Collaboration, focused on the study on non-trivial topology on cosmological observables. 

I am part of the Euclid Collaboration, an ESA mission to map the extragalactic sky and the distribution of matter.