Exploration of extreme QCD matter with deep learning

Abstract

To study hot and dense nuclear matter, relativistic nuclear collisions are carried out experimentally, while lattice field theory provides a first-principles investigation. Meanwhile, astronomical observations of neutron stars also provide constraints on cold and dense nuclear matter. In this talk, I present the potential of deep learning based strategies to aid the exploration of QCD matter under extreme conditions, ranging from identifying essential physics from nuclear collision experiments, to facilitating lattice QCD data analysis, to efficiently exploiting astronomical observations in extracting the dense matter equation of state.