Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
Statistical hadronization of heavy quarks in ultra-relativistic nucleus-nucleus collisions
3 Pith papers cite this work. Polarity classification is still indexing.
abstract
We present new results on the statistical hadronization of heavy quarks at SPS, RHIC and LHC energies. Several new aspects are considered, among them a separation of the collision geometry into a ``core'' and a ``corona'' part and an estimate of the annihilation rate of charm quark in a hot plasma, together with a critical assessment of its influence on the results. For RHIC energies we investigate the centrality dependence of J/psi production focusing on the model results for different values of the charm production cross section, including its theoretical and experimental uncertainty. We also study, within this model, the rapidity dependence of the J/psi yield. Recent RHIC data from the PHENIX experiment are well reproduced. At LHC energy, we update our model predictions for the centrality dependence of the J/psi yield and investigate as well the rapidity dependence. We also discuss the transverse momentum distributions of J/psi mesons expected from the model and provide predictions for a range of values of the expansion velocity at chemical freeze-out. Finally, we extend the model to predict Upsilon yields in Pb+Pb collisions at LHC energy.
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Charm quark production from the pre-equilibrium phase contributes non-negligibly to the total yield in heavy-ion collisions and may allow inference of pre-equilibrium properties when combined with better initial-state calculations.
The statistical hadronization model successfully describes hadron production in nuclear collisions over broad energies, with implications for QCD phase structure.
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Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning
Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
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Charm quark production in heavy-ion collisions as a signature of pre-equilibrium
Charm quark production from the pre-equilibrium phase contributes non-negligibly to the total yield in heavy-ion collisions and may allow inference of pre-equilibrium properties when combined with better initial-state calculations.
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Statistical hadronization: successes and some open issues
The statistical hadronization model successfully describes hadron production in nuclear collisions over broad energies, with implications for QCD phase structure.