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Register a new userGluon saturation and initial conditions for relativistic heavy ion collisions
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We present an overview of theoretical aspects of the phenomenon of gluon saturation in high energy scattering in Quantum Chromo Dynamics. Then we review the state-of-the-art of saturation-based phenomenological approaches to the study and characterisation of the initial state of ultra-relativistic heavy ion collisions performed at RHIC and the LHC. Our review focuses mostly in the Color Glass Condensate effective theory, although we shall also discuss other approaches in parallel.
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We estimate the energy density and the gluon distribution associated with the classical fields describing the early-time dynamics of the heavy-ion collisions. We first decompose the energy density into the momentum components exactly in the McLerran-Venugopalan model, with the use of the Wilson line correlators. Then we evolve the energy density with the free-field equation, which is justified by the dominance of the ultraviolet modes near the collision point. We also discuss the improvement with inclusion of nonlinear terms into the time evolution. Our numerical results at RHIC energy are fairly consistent with the empirical values.
$alpha$-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial non-clustered and clustered configurations, namely Woods-Saxon distribution and three-$alpha$ triangular (four-$alpha$ tetrahedral) structure for $^{12}$C ($^{16}$O), from heavy-ion collision events generated within a multi-phase transport (AMPT) model. Azimuthal angle and transverse momentum distributions of charged pions are taken as inputs to train the classifier. On multiple-event basis, the overall classification accuracy can reach $95%$ for $^{12}$C/$^{16}$O + $^{197}$Au events at $sqrt{S_{NN}} =$ 200 GeV. With proper constructions of samples, the predicted deviations on mixed samples with different proportions of both configurations could be within $5%$. In addition, setting a simple confidence threshold can further improve the predictions on the mixed dataset. Our results indicate promising and extensive possibilities of application of machine-learning-based techniques to real data and some other problems in physics of heavy-ion collisions.
We present a brief review of recent theoretical developments and related phenomenological approaches for understanding the initial state of heavy-ion collisions, with emphasis on the Color Glass Condensate formalism.
Coupling hadronic kinetic theory models to fluid dynamics in phenomenological studies of heavy ion collisions requires a prescription for ``particlization. Existing particlization models are based on implicit or explicit assumptions about the microscopic degrees of freedom that go beyond the information provided by the preceding fluid dynamical history. We propose an alternative prescription which uses only macroscopic information provided by the hydrodynamic output. This method follows directly from the connections between information theory and statistical mechanics.
Relativistic models can be successfully applied to the description of compact star properties in nuclear astrophysics as well as to nuclear matter and finite nuclei properties, these studies taking place at low and moderate temperatures. Nevertheless, all results are model dependent and so far it is unclear whether some of them should be discarded. Moreover, in the regime of hot hadronic matter very few calculations exist using these relativistic models, in particular when applied to particle yields in heavy ion collisions. In the present work we comment on the known constraints that can help the selection of adequate models in this regime and investigate the main differences that arise when the particle production during a Au+Au collision at RHIC is calculated with different models.
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