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Institute
We study anisotropic fluid dynamics derived from the Boltzmann equation based on a particular choice for the anisotropic distribution function within a boost-invariant expansion of the fluid in one spatial dimension. In order to close the conservation equations we need to choose an additional moment of the Boltzmann equation. We discuss the influence of this choice of closure on the time evolution of fluid-dynamical variables and search for the best agreement to the solution of the Boltzmann equation in the relaxation-time approximation.
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff Esym(ρ) is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different Esym(ρ)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of Esym(ρ)) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm.
The production of strange pentaquark states (e.g., Theta baryons and Ξ−− states) in hadronic interactions within a Gribov–Regge approach is explored. In this approach the Θ+(1540) and the Ξ are produced by disintegration of remnants formed by the exchange of pomerons between the two protons. We predict the rapidity and transverse momentum distributions as well as the 4π multiplicity of the Θ+, Ξ−−, Ξ−, Ξ0 and Ξ+ for s=17 GeV (SPS) and 200 GeV (RHIC). For both energies more than 10−3 Θ+ and more than 10−5 Ξ per pp event should be observed by the present experiments.
Within a dynamical quark recombination model, we explore various proposed event-by-event observables sensitive to the microscopic structure of the QCD-matter created at RHIC energies. Charge ratio fluctuations, charge transfer fluctuations and baryon-strangeness correlations are computed from a sample of central Au + Au events at the highest RHIC energy available (sNN=200 GeV). We find that for all explored observables, the calculations yield the values predicted for a quark–gluon plasma only at early times of the evolution, whereas the final state approaches the values expected for a hadronic gas. We argue that the recombination-like hadronization process itself is responsible for the disappearance of the predicted deconfinement signals. This might explain why no fluctuation signatures for the transition between quark and hadronic matter was ever observed in the experimental data up to now.
The ALICE detector is ideally suited to study the production of anti- and hyper-matter due to its excellent particle identification capabilities. The measurement of the He¯4-nucleus in Pb–Pb collisons at sNN=2.76TeV is presented. We further show the performance for the reconstruction of the (anti-)hypertriton in the decay to He3+π− (He¯3+π+). In addition to this, two searches have been performed, one for the H-Dibaryon →Λpπ− and one for the Λn bound state (Λn¯→d¯π+). No signals are observed for these exotic states and upper limits have been determined.
The production of light neutral mesons in AA collisions probes the physics of the Quark-Gluon Plasma (QGP), which is formed in heavy-ion collisions at the LHC. More specifically, the centrality dependent neutral meson spectra in AA collisions compared to its spectra in minimum-bias pp collisions, scaled with the number of hard collisions, provides information on the energy loss of partons traversing the QGP. The measurement allows to test with high precision the predictions of theoretical model calculations. In addition, the decay of the π0 and η mesons are the dominant back- grounds for all direct photon measurements. Therefore, pushing the limits of the precision of neutral meson production is key to learning about the temperature and space-time evolution of the QGP.
In the ALICE experiment neutral mesons can be detected via their decay into two photons. The latter can be reconstructed using the two calorimeters EMCal and PHOS or via conversions in the detector material. The excellent momentum resolution of the conversion photons down to very low pT and the high reconstruction efficiency and triggering capability of calorimeters at high pT, allow us to measure the pT dependent invariant yield of light neutral mesons over a wide kinematic range.
Combining state-of-the-art reconstruction techniques with the high statistics delivered by the LHC in Run 2 gives us the opportunity to enhance the precision of our measurements. In these proceedings, new ALICE run 2 preliminary results for neutral meson production in pp and Pb–Pb collisions at LHC energies are presented.
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.
The upcoming high energy experiments at the LHC are one of the most outstanding efforts for a better understanding of nature. It is associated with great hopes in the physics community. But there is also some fear in the public, that the conjectured production of mini black holes might lead to a dangerous chain reaction. In this Letter we summarize the most straightforward arguments that are necessary to rule out such doomsday scenarios.