A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data statistics with approximately six orders of magnitude computational speedup.
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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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A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions
A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data statistics with approximately six orders of magnitude computational speedup.
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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.