Machine learning and big data in Physics
21 January 2021 - Online event
Extending Galactic models for Cosmic Microwave Background emission with adversarial nets

Abstract
The last few decades represented this research focuses on the Cosmic Microwave Background (CMB) radiation, the light relic of the Big Bang. One of the major challenges in this context is to detect a polarization pattern, the so called B-modes of CMB polarization, that are thought to be directly linked to the space-time fluctuations present in the Universe at the very first instants of life. To date, several challenges have prevented to detect the B-modes partly because of the lower sensitivity of the detectors. Our own Galaxy is observed in this context as a foreground contamination. In this talk we will show a novel technique encoding generative neural networks aimed at improving the modeling of the polarized emission at sub-millimetric wavelengths, mostly sourced by the Galactic dust.