Responsabile: Carmelita Carbone

Descrizione: Cosmic voids are the largest (non-virialized) structures in the Universe. The volume distribution of cosmic voids is a recent cosmological probe given the almost full-sky areas of ongoing and forthcoming galaxy surveys.  The evolution of linear initial conditions present in the early universe into extended cosmic voids at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process and dependency on the cosmological parameters remains elusive. The aim of this thesis project is to build a deep learning framework to investigate these questions. In particular, we will train a neural network to predict the volume of cosmic voids from the initial conditions in the presence of massive neutrinos and to quantify the impact of cosmic expansion of the Universe in the presence of dark energy.

Durata: 6/9 Mesi

Laurea: Magistrale

Prerequisiti: Esame di cosmologia e nozioni di programmazione