No abstract

No abstract

We review the properties of the strongly interacting quark-gluon plasma (QGP) at finite temperature T and baryon chemical potential B as created in heavy-ion collisions at ultrarelativistic energies. The description of the strongly interacting (non-perturbative) QGP in equilibrium is based on the effective propagators and couplings from the Dynamical QuasiParticle Model (DQPM) that is matched to reproduce the equation-of-state of the partonic system above the deconfinement temperature T C from lattice QCD. Based on a microscopic transport description of heavy-ion collisions, we discuss which observables are sensitive to the QGP creation and its properties. K E Y W O R D Sheavy-ions, quark-gluon plasma, transport models quark-gluon plasma INTRODUCTIONAn understanding of the structure of our universe is an intriguing topic of research in our Millennium, which combines the efforts of physicists working in different fields of astrophysics, cosmology, and heavy-ion physics (Strassmeier et al. 2019). The common theoretical efforts and modern achievements in experimental physicsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Vacuum fluctuations of quantum fields between physical objects depend on the shapes, positions, and internal composition of the latter. For objects of arbitrary shapes, even made from idealized materials, the calculation of the associated zero-point (Casimir) energy is an analytically intractable challenge. We propose a different numerical approach to this problem based on machine-learning techniques and illustrate the effectiveness of the method in a (2+1)-dimensional scalar field theory. The Casimir energy is first calculated numerically using a Monte Carlo algorithm for a set of the Dirichlet boundaries of various shapes. Then, a neural network is trained to compute this energy given the Dirichlet domain, treating the latter as black-and-white pixelated images. We show that after the learning phase, the neural network is able to quickly predict the Casimir energy for new boundaries of general shapes with reasonable accuracy.

No abstract