Speaker
Description
I will present and describe the CAMELS project, whose aim is to build bridges between cosmology and galaxy formation by combining numerical simulations and machine learning. Containing a set of more than 10,000 simulations, both N-body and state-of-the-art hydrodynamic simulations, it is currently the largest dataset of cosmological simulations designed to train artificial intelligence algorithms. I will present some of the results the CAMELS collaboration has obtained recently, such as the finding of a universal relation in subhalo properties, how neural networks can extract cosmological information while marginalizing over baryonic effects at the field level, the first constraints on the mass of the Milky Way and Andromeda from AI, and the prospects of doing cosmology with individual galaxies. I will conclude by presenting a potential strategy the scientific community may pursue to extract the maximum amount of information from cosmological surveys, highlighting the multiple challenges associated with it.
