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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.
In this work we study compact stars, i.e. neutron stars, as cosmic laboratories for the nuclear matter. With a mass of around 1 - 3 solar masses and a radius of around 10km, compact stars are very dense and, besides nucleons, can contain exotic matter such as hyperons or quark matter. The KaoS collaboration studied nuclear matter for densities up to 2-3 times saturation density by analysing kaon multiplicities from Au+Au and C+C collisions. The results show that nuclear matter in the corresponding density region is very compressible, with a compressibility of <200MeV. For such soft nuclear equations of state the maximum masses of neutron stars are ca. 1.8 - 1.9 solar masses, whereas the central densities are higher than 5 times nuclear saturation density and therefore point towards a possible phase transition to quark matter. If quark matter would be present in the interior of neutron stars, so-called hybrid stars, it could be produced already during their birth in supernova explosions. To study this we implement a quark matter phase transition in a hadronic equation of state which is used in supernova simulations. Supernova simulations of low and intermediate mass progenitors and two different bag constants show a collapse of the proto neutron star due to the softening of the equations of state in the quark-hadron mixed phase. The stiffening of the equation of state for pure quark matter halts the collapse and leads to the production of a second shock wave. The second shock wave is energetic enough to lead to an explosion of the star and produces a neutrino burst when passing the neutrinospheres. Furthermore, first studies of the longtime cooling of hybrid stars show, that colour superconductivity can significantly influence the cooling behaviour of hybrid stars, if all quarks form Cooper Pairs. For the so-called CSL phase (colour-spin locking) with pairing energies of several MeV, the cooling of the quark phase is suppressed and the hybrid star appears as a pure hadronic star.