Speaker
Description
The detection of the primordial B-mode polarization in the Cosmic Microwave Background (CMB) is one of the most compelling goals in modern cosmology, as it would provide strong evidence for an inflationary period. Achieving this requires accurate component separation techniques to disentangle the cosmological signal from astrophysical foregrounds and instrumental noise. In this work, we present the development of a method based on Neural Networks (NN) to address this challenge. The proposed approach is pixel-based, making it independent of the underlying sky geometry and thus suitable for working directly on spherical data. It is designed to recover the different sky components, including the CMB and polarized galactic components. In the first step, we apply this method to simulated observations from a next-generation CMB experiment, presenting some preliminary results on its performance.