Frontiers of perturbative thermal QCD in extreme systems

Objective

Predicting the thermodynamic and transport properties of strongly interacting matter at the highest densities in the present-day Universe is a pressing challenge bridging modern particle and astrophysics. Determining whether deconfined Quark Matter (QM), the high-density counterpart of the Quark-Gluon Plasma created in heavy-ion collisions, exists or forms in Neutron Stars (NSs) and their mergers would be a landmark result. However, this task is severely complicated by the Sign Problem of lattice QCD, preventing the direct use of nonperturbative methods. The ExPertQCD project will significantly advance our quantitative understanding of deconfined QM in NS conditions by employing cutting-edge perturbative thermal field theory methods. A critical challenge is the need for high-precision perturbative QCD calculations of the four-loop Equation of State (EOS) for both cold and warm QM, along with an accurate determination of the QM bulk viscosity. These milestone results will greatly improve the precision with which we understand the properties of NS matter and provide first-principles input for modern NS merger simulations. To achieve these goals, the project will overcome limitations of traditional perturbative techniques. The key innovation lies in applying Loop Tree Duality (LTD), a powerful algorithmic method from collider physics, to address computations with nonzero chemical potentials and temperatures. This method enables the treatment of all thermal Feynman diagrams up to the four-loop order, previously considered impossible to evaluate. By pushing the limits of perturbative thermal field theory with LTD, the project has the potential to significantly advance the description of early-Universe thermodynamics and thermal particle production rates. These advances could yield the most accurate predictions of strong phase transitions, critical for gravitational wave analysis in future experiments like LISA, and improved calculations of beyond Standard Model rates.