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In this thesis, the early time dynamics in a heavy ion collision of Pb-Nuclei at LHC center-of-mass energies of 5 TeV is studied. Right after the collision the system is out-of-equilibrium and essentially gluon dominated, with their density saturating at a specific momentum scale Q_s. Based on a separation of scales for the soft and hard gluonic degrees of freedom, the initial state is given from an effective model, known as the Color Glass Condensate. Within this model, the soft gluons behave classical to leading order, making it possible to study their dynamics in gauge invariant fashion on a three dimensional lattice, solving Hamiltonian field equations of motion, keeping real time. Quark-Antiquark pairs are produced in the gluonic medium, known as the Glasma and manifest themselves as a source of quantum fluctuations.
They enter the dynamics of the gluons as a current, making the system semi-classical. In lattice simulations, the non-equilibrium system is tested for pressure isotropization, which is a necessary ingredient to reach a local thermal equilibrium (LTE), making a hydrodynamical description at a later stage possible. In addition, the occupation of energy modes is studied with its implications on thermalization and classicality.
This work focuses on the investigation of K+, K- and ϕ-meson production in Ag(1.58 A GeV)+Ag collisions. The energetically cheapest channel for direct K+ production in binary NN-collisions NN→NΛK+ lies at exactly this energy. For the remaining K- and ϕ-mesons, an excess energy of 0.31 GeV and 0.34 GeV in the centre of mass system has to be provided by the system. This makes these particles an excellent probe for effects inside the medium.
K+ and K- mesons can be reconstructed directly as they possess a cτ of approximately 3.7 m. Using the approximately 3 billion recorded Ag(1.58 A GeV)+Ag 0-30% most central collision events, all reconstructed K+ and K- within the detector acceptance are investigated for their kinematic properties and their particle production rates compared to a selection of existing models.
Artificial intelligence in heavy-ion collisions : bridging the gap between theory and experiments
(2023)
Artificial Intelligence (AI) methods are employed to study heavy-ion collisions at intermediate collision energies, where high baryon density and moderate temperature QCD matter is produced. The experimental measurements of various conventional observables such as collective flow, particle number fluctuations, etc. are usually compared with expensive model calculations to infer the physics governing the evolution of the matter produced in the collisions. Various experimental effects and processing algorithms can greatly affect the sensitivity of these observables. AI methods are used to bridge this gap between theory and experiments of heavy-ion collisions. The problems with conventional methods of analyzing experimental data are illustrated in a comparative study of the Glauber MC model and the UrQMD transport model. It is found that the centrality determination and the estimated fluctuations of the number of participant nucleons suffer from strong model dependencies for Au-Au collisions at 1.23 AGeV. This can bias the results of the experimental analysis if the number of participant nucleons used is not consistent throughout the analysis and in the final model-to-data comparison. The measurable consequences of this model dependence of the number of participant nucleons are also discussed. In this context, PointNet-based AI models are developed to accurately reconstruct the impact parameter or the number of participant nucleons in a collision event from the hits and/or reconstructed track of particles in 10 AGeV Au-Au collisions at the CBM experiment. In the last part of the thesis, different AI methods to study the equation of state (EoS) at high baryon densities are discussed. First, a Bayesian inference is performed to constrain the density dependence of the EoS from the available experimental measurements of elliptical flow and mean transverse kinetic energy of mid rapidity protons in intermediate energy collisions. The UrQMD model was augmented to include arbitrary potentials (or equivalently the EoSs) in the QMD part to provide a consistent treatment of the EoS throughout the evolution of the system. The experimental data constrain the posterior constructed for the EoS for densities up to four times saturation density. However, beyond three times saturation density, the shape of the posterior depends on the choice of observables used. There is a tension in the measurements at a collision energy of about 4 GeV. This could indicate large uncertainties in the measurements, or alternatively the inability of the underlying model to describe the observables with a given input EoS. Tighter constraints and fully conclusive statements on the EoS require accurate, high statistics data in the whole beam energy range of 2-10 GeV, which will hopefully be provided by the beam energy scan programme of STAR-FXT at RHIC, the upcoming CBM experiment at FAIR, and future experiments at HIAF and NICA. Finally, it is shown that the PointNet-based models can also be used to identify the equation of state in the CBM experiment. Despite the uncertainties due to limited detector acceptance and biases in the reconstruction algorithms, the PointNet-based models are able to learn the features that can accurately identify the underlying physics of the collision. The PointNet-based models are an ideal AI tool to study heavy-ion collisions, not only to identify the geometric event features, such as the impact parameter or the number of participant nucleons, but also to extract abstract physical features, such as the EoS, directly from the detector outputs.