CTP Seminar
Due to the unprecedented sensitivity and large field of views, extracting the maximum amount of information remains a key challenge in future surveys. In this talk, I will present different promising methods to constrain the physics of the first light epoch and intensity mapping. In particular, I will discuss the implications of the UV radiation background inferred by recent JWST observations for radio experiments aimed at detecting the redshifted 21-cm hyperfine transition of diffuse neutral hydrogen. I will show how JWST observations can be used to place constraints on the presence of a 21 cm signal as well as the excess of radio background at Cosmic Dawn. This first part of the talk will highlight the importance of connecting different experiments to perform joint analysis to maximize the scientific return of future surveys. The second part will focus on the use of Machine Learning in cosmology. I will present several machine learning models and techniques that are capable of simultaneously generating new synthetic diverse examples of large-scale HI maps, and enabling parameter inference at the field level. These new tools represent the first step toward a more powerful parameter inference, maximizing the scientific return of future HI surveys, and opening a new avenue to minimize the loss of complex information due to data compression down to summary statistics.