To realize the advances in cosmological knowledge we desire in the coming decade will require a new way for cosmological theory, simulation, and inference to interplay. As cosmologists we wish to learn about the origin, composition, evolution, and fate of the cosmos from all accessible sources of astronomical data, such as the cosmic microwave background, galaxy surveys, or electromagnetic and gravitational wave transients. Traditionally, the field has progressed by designing, modeling and measuring intuitive summaries of the data, such as 2-point correlations. This traditional approach has a number of risks and limitations: how do we know if we computed the most informative statistics? Did we omit any summaries that would have provided additional information or break parameter degeneracies? Are current approximations to the likelihood and physical modeling sufficient? I will discuss simulation-based, full-physics modeling approaches to cosmology that are powered by new ways of designing and running simulations of cosmological observables and of comparing models to data. Innovative machine-learning methodology plays an important role in making this possible. The goal is to use current and next-generation data to reconstruct the cosmological initial conditions; and constrain cosmological physics much more completely than has been feasible in the past. I will discuss the current status and challenges of this new approach.
Nicola Bartolo, Daniele Bertacca, Michele Liguori, Sabino Matarrese, Alvise Raccanelli, Angelo Ricciardone
