Machine Learning in Quantum Field Theories: Phase Transitions and Finite Density

Strongly interacting systems at finite temperature and/or density may have an intricate phase structure, which is difficult to determine or characterise, with QCD at nonzero density being a prominent example. This may be due to the non-positivity of the measure of the path integral defining the theory, known as the sign problem, or the absence of order parameters. The aim of this project is to investigate and improve novel approaches to hot and dense quantum systems such as complex Langevin dynamics and density of states for theories with a sign problem, using machine learning for phase identification. The project will develop through calculations on simpler models and culminate with studies of physically relevant systems such as Quantum Chromodynamics at finite density.

Supervisors: Gert Arts, Biagio Lucini (Swansea University)