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A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9% and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10% and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.
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.
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.
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the 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 quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
Relying on the existing estimates for the production cross sections of mini black holes in models with large extra dimensions, we review strategies for identifying those objects at collider experiments. We further consider a possible stable final state of such black holes and discuss their characteristic signatures. Keywords: Black holes
The transverse momentum dependence of the anisotropic flow v_2 for pi, K, nucleon, Lambda, Xi and Omega is studied for Au+Au collisions at sqrt s_NN = 200 GeV within two independent string-hadron transport approaches (RQMD and UrQMD). Although both models reach only 60% of the absolute magnitude of the measured v_2, they both predict the particle type dependence of v_2, as observed by the RHIC experiments: v_2 exhibits a hadron-mass hierarchy (HMH) in the low p_T region and a number-of-constituent-quark (NCQ) dependence in the intermediate p_T region. The failure of the hadronic models to reproduce the absolute magnitude of the observed v_2 indicates that transport calculations of heavy ion collisions at RHIC must incorporate interactions among quarks and gluons in the early, hot and dense phase. The presence of an NCQ scaling in the string-hadron model results suggests that the particle-type dependencies observed in heavy-ion collisions at intermediate p_T are related to the hadronic cross sections in vacuum rather than to the hadronization process itself, as suggested by quark recombination models.
Antibaryons bound in nuclei
(2004)
We study the possibility of producing a new kind of nuclear systems which in addition to ordinary nucleons contain a few antibaryons (B = p, , etc.). The properties of such systems are described within the relativistic mean field model by employing G parity transformed interactions for antibaryons. Calculations are first done for infinite systems and then for finite nuclei from 4He to 208Pb. It is demonstrated that the presence of a real antibaryon leads to a strong rearrangement of a target nucleus resulting in a significant increase of its binding energy and local compression. Noticeable e ects remain even after the antibaryon coupling constants are reduced by factor 3 4 compared to G parity motivated values. We have performed detailed calculations of the antibaryon annihilation rates in the nuclear environment by applying a kinetic approach. It is shown that due to significant reduction of the reaction Q values, the in medium annihilation rates should be strongly suppressed leading to relatively long lived antibaryon nucleus systems. Multi nucleon annihilation channels are analyzed too. We have also estimated formation probabilities of bound B + A systems in pA reactions and have found that their observation will be feasible at the future GSI antiproton facility. Several observable signatures are proposed. The possibility of producing multi quark antiquark clusters is discussed. PACS numbers: 25.43.+t, 21.10.-k, 21.30.Fe, 21.80.+a
One of important consequences of Hagedorn statistical bootstrap model is the prediction of limiting temperature Tcrit for hadron systems colloquially known as Hagedorn temperature. According to Hagedorn, this effect should be observed in hadron spectra obtained in infinite equilibrated nuclear matter rather than in relativistic heavy-ion collisions. We present results of microscopic model calculations for the infinite nuclear matter, simulated by a box with periodic boundary conditions. The limiting temperature indeed appears in the model calculations. Its origin is traced to strings and many-body decays of resonances.
Event-by-event fluctuations of the net baryon number and electric charge in nucleus-nucleus collisions are studied in Pb+Pb at SPS energies within the HSD transport model. We reveal an important role of the fluctuations in the number of target nucleon participants. They strongly influence all measured fluctuations even in the samples of events with rather rigid centrality trigger. This fact can be used to check different scenarios of nucleus-nucleus collisions by measuring the multiplicity fluctuations as a function of collision centrality in fixed kinematical regions of the projectile and target hemispheres. The HSD results for the event-by-event fluctuations of electric charge in central Pb+Pb collisions at 20, 30, 40, 80 and 158 A GeV are in a good agreement with the NA49 experimental data and considerably larger than expected in a quark-gluon plasma. This demonstrate that the distortions of the initial fluctuations by the hadronization phase and, in particular, by the final resonance decays dominate the observable fluctuations.
The interplay of charmonium production and suppression in In+In and Pb+Pb reactions at 158 AGeV and in Au+Au reactions at sqrt(s)=200 GeV is investigated with the HSD transport approach within the hadronic comover model' and the QGP melting scenario'. The results for the J/Psi suppression and the Psi' to J/Psi ratio are compared to the recent data of the NA50, NA60, and PHENIX Collaborations. We find that, at 158 AGeV, the comover absorption model performs better than the scenario of abrupt threshold melting. However, neither interaction with hadrons alone nor simple color screening satisfactory describes the data at sqrt(s)=200 GeV. A deconfined phase is clearly reached at RHIC, but a theory having the relevant degrees of freedom in this regime (strongly interacting quarks/gluons) is needed to study its transport properties.