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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A time-dependent Schrödinger equation model reproduces suppression of Υ(nS)/Υ(1S) and ψ(2S)/J/ψ yield ratios versus multiplicity in p-Pb collisions at 8.16 TeV, supporting transient hot QCD medium in small systems.
Event-by-event hydrodynamic fluctuations have marginal effects on bottomonium R_AA and v2 in 5.02 TeV Pb-Pb collisions.
citing papers explorer
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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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Probing hot QCD medium with heavy quarkonium in small and large collision systems
A time-dependent Schrödinger equation model reproduces suppression of Υ(nS)/Υ(1S) and ψ(2S)/J/ψ yield ratios versus multiplicity in p-Pb collisions at 8.16 TeV, supporting transient hot QCD medium in small systems.
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Effects of event-by-event hydrodynamic fluctuations on bottomonium dynamics in Pb--Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV
Event-by-event hydrodynamic fluctuations have marginal effects on bottomonium R_AA and v2 in 5.02 TeV Pb-Pb collisions.