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The QCD equation of state at finite baryon density is studied in the framework of a Cluster Expansion Model (CEM), which is based on the fugacity expansion of the net baryon density. The CEM uses the two leading Fourier coefficients, obtained from lattice simulations at imaginary μB, as the only model input and permits a closed analytic form. Excellent description of the available lattice data at both μB = 0 and at imaginary μB is obtained. We also demonstrate how the Fourier coefficients can be reconstructed from baryon number susceptibilities.
The effect of nuclear interactions on measurable net-proton number fluctuations in heavy ion collisions at the SIS18/GSI accelerator is investigated. The state of the art UrQMD model including interaction potentials is employed. It is found that the nuclear forces enhance the baryon number cumulants, as predicted from grand canonical thermodynamical models. The effect however is smeared out for proton number fluctuations due to iso-spin randomization and global baryon number conservation, which decreases the cumulant ratios. For a rapidity acceptance window larger than Δy > 0.4 the effects of global baryon number conservation dominate and all cumulant ratios are significantly smaller than 1.
We study the correlation between the distributions of the net-charge, net-kaon, net-baryon and net-proton number at hadronization and after the final hadronic decoupling by simulating ultra relativistic heavy ion collisions with the hybrid version of the ultrarelativistic quantum molecular dynamics (UrQMD) model. We find that due to the hadronic rescattering these distributions are not strongly correlated. The calculated change of the correlation, during the hadronic expansion stage, does not support the recent paradigm, namely that the measured final moments of the experimentally observed distributions do give directly the values of those distributions at earlier times, when the system had been closer to the QCD crossover.
The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differ- entiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such sce- narios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.
Results on proton and Λ flow, calculated with the UrQMD model that incorporates different realistic density dependent equations of state, are presented. It is shown that the proton and hyperon flow shows sensitivity to the equation of state and especially to the appearance of a phase transition at densities below 4n0. Even though qualitatively hyperons and protons exhibit the same beam energy dependence of the flow, the quantitative results are different. In this context it is suggested that the hyperon measurements can be used to study the density dependence of the hyperon interaction in high density QCD matter.
We apply a coupled transport-hydrodynamics model to discuss the production of multi-strange meta-stable objects in Pb + Pb reactions at the FAIR facility. In addition to making predictions for yields of these particles we are able to calculate particle dependent rapidity and momentum distributions. We argue that the FAIR energy regime is the optimal place to search for multi-strange baryonic object (due to the high baryon density, favoring a distillation of strangeness). Additionally, we show results for strangeness and baryon density fluctuations. Using the UrQMD model we calculate the strangeness separation in phase space which might lead to an enhanced production of MEMOs compared to models that assume global thermalization.
We point out that the variance of net-baryon distribution normalized by the Skellam distribution baseline, κ2[B−B¯]/〈B+B¯〉, is sensitive to the possible modification of (anti)baryon yields due to BB¯ annihilation in the hadronic phase. The corresponding measurements can thus place stringent limits on the magnitude of the BB¯ annihilation and its inverse reaction. We perform Monte Carlo simulations of the hadronic phase in Pb-Pb collisions at the LHC via the recently developed subensemble sampler + UrQMD afterburner and show that the effect survives in net-proton fluctuations, which are directly accessible experimentally. The available experimental data of the ALICE Collaboration on net-proton fluctuations disfavors a notable suppression of (anti)baryon yields in BB¯ annihilations predicted by the present version of UrQMD if only global baryon conservation is incorporated. On the other hand, the annihilations improve the data description when local baryon conservation is imposed. The two effects can be disentangled by measuring κ2[B+B¯]/〈B+B¯〉, which at the LHC is notably suppressed by annihilations but virtually unaffected by baryon number conservation.
In this work the baryon number and strange susceptibility of second and fourth order are presented. The results at zero baryon-chemical potential are obtained using a well tested chiral effective model including all known hadron degrees of freedom and additionally implementing quarks and gluons in a PNJL-like approach. Quark and baryon number susceptibilities are sensitive to the fundamental degrees of freedom in the model and signal the shift from massive hadrons to light quarks at the deconfinement transition by a sharp rise at the critical temperature. Furthermore, all susceptibilities are found to be largely suppressed by repulsive vector field interactions of the particles. In the hadronic sector vector repulsion of baryon resonances restrains fluctuations to a large amount and in the quark sector above Tc even small vector field interactions of quarks quench all fluctuations unreasonably strong. For this reason, vector field interactions for quarks have to vanish in the deconfinement limit.
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.