Ricerc@Sapienza

Astrophysical data sets are increasing in volume and in information density. The upward trend will continue thanks to ground facilities and space observatories, such as the ESA Ariel and Euclid space missions beginning operations within the current decade. This provides us opportunities to investigate from Galactic scales up to cosmological scales with unprecedented accuracy. Spectroscopic surveys of hundreds of exoplanetary atmospheres with Ariel will allow us to explore our Galactic neighbourhood extending, for the first time, comparative planetology beyond the Solar System boundary. The increasing accuracy of cosmological parameters estimates, a trend expected to continue with Euclid, is manifesting tensions between theory and observations. A thrilling opportunity that can be the signal of new physics, or the consequence of systematics that are no longer negligible as error bars shrink.

Astronomical data increasingly exhibit challenges shared with big data: large volumes, high dimensionality, models that are non linear in their parameters or that depend on parameters that cannot be directly constrained from observations. Making progress requires developing new data analysis techniques capable of coping with the rising data complexity.

This project aims at bringing together expertise in exoplanetary characterisation, and cosmology with galaxy clusters, lensing and CMB probes, to address the shared challenge and opportunity of large astrophysical dataset. The investigation will use observations and simulations to assess the performance of machine learning (ML) and Artificial Intelligence (AI) techniques, as well as advanced statistical methods for: i) the chemical characterisation of the atmospheres of 100s of exoplanets; ii) the estimation of the masses of cluster of galaxies needed for cosmological probes; iii) the estimation of systematics and higher order statistic properties of cosmic shear from lensing observations with Euclid.