Simone Paradiso is a postdoc at the Waterloo Centre for Astrophysics at University of Waterloo. Originally from Rome, in 2016 he completed his Bachelors and Masters degree at University of Rome “La Sapienza” in Astrophysics. He completed his PhD in Physics and Astrophysics in 2021 at University of Milan. Simone’s research focuses on physical and statistical cosmology, by studying the cosmic microwave background. His most recent accomplishment in this field has been within the BeyondPlanck project, an EU sponsored initiative which delivered a full Bayesian end-to-end analysis of the Planck data. Simone is now working on developing novel statistical techniques to be applied to cosmological data; he is also part of the Large Scale Polarization Explorer and the QUBIC CMB experiments, as well as actively contributing to the Cosmoglobe project, a natural follow-up of BeyondPlanck.
Title: CMB analysis within a Bayesian end-to-end framework
Abstract: The cosmic microwave background constitutes one of the most powerful probes of cosmology available today, as the statistical properties of the pattern of small variations in the intensity and polarisation of this radiation impose strong constraints on cosmological structure formation processes in the early universe. State-of-the-art full-sky CMB measurements from the Planck satellite, complemented by ground and balloon observations and data from non-CMB cosmological probes have led to a spectacularly successful cosmological concordance model called ΛCDM. This model is able to describe a host of cosmological observables with exquisite precision, although it leaves much to be desired in terms of theoretical understanding. Next generation CMB experiments aim to measure the very faint signal coming from primordial gravitational waves produced during the Inflation, and the only way to achieve such a goal is both to increase the sensitivity of instrumentation, and to develop a highly reliable methodology to control systematic effects and component separation residuals. In this talk, I will therefore present my contribution to this very last aspect. Within the BeyondPlanck project, we developed and run a complete Bayesian end-to-end analysis of the Planck LFI data. The main objective was to sample the whole parameter space in order to seamlessly propagate uncertainties throughout the pipeline, and characterize the marginalised posterior distribution of the cosmological parameters over all the sources of uncertainty.