Cosmology studies some of the most fundamental questions in modern physics: What makes up most of the energy density of the Universe? How did the Universe evolve from its initial to its present state? Is gravity described by General Relativity?, to only name a few.
Recent progress in observational cosmology and the establishment of ΛCDM have relied on the combination of different cosmological probes. These probes are not independent, since they all measure the same physical fields. The resulting cross-correlations allow for a robust test of the cosmological model through the consistency of different physical tracers and for the identification of systematics.
Observational cosmology is currently undergoing transformational changes, as a number of high-precision, wide-field surveys will start operations in the mid- to late 2020s; examples include Rubin/LSST, Euclid, Roman, the Simons Observatory, and CMB S4. These surveys will give us the opportunity to test the foundations of our cosmological model. However, the increase in survey sensitivity implies that controlling systematic biases and uncertainties will become the main challenge in their analysis, therefore requiring novel data analysis methods.
Andrina Nicola’s research combines data from several cosmological probes, such as the CMB, galaxy clustering and weak gravitational lensing, to test and constrain our cosmological model and the physics of galaxy formation. In particular, Andrina is working on developing novel data analysis methods making use of Machine Learning and Artificial Intelligence to optimally benefit from these exciting data sets and test the foundations of our cosmological model.