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 physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
A PINN-trained quasi-parton model reproduces lattice cumulants at vanishing chemical potentials and supplies a consistent four-dimensional QCD equation of state at finite densities.
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 Proton Structure via Physics-Guided Neural Networks in Holographic QCD
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
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Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
A PINN-trained quasi-parton model reproduces lattice cumulants at vanishing chemical potentials and supplies a consistent four-dimensional QCD equation of state at finite densities.
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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.