Applications of deep learning to relativistic hydrodynamics

Abstract

Relativistic hydrodynamics is a powerful tool to simulate the evolution of the quark gluon plasma (QGP) in relativistic heavy ion collisions. Using 10000 initial and final profiles generated from 2+1-d relativistic hydrodynamics VISH2+1 with MC-Glauber initial conditions, we train a deep neural network based on stacked U-net, and use it to predict the final profiles associated with various initial conditions, including MC-Glauber, MC-KLN and AMPT and TRENTo. A comparison with the VISH2+1 results shows that the network predictions can nicely capture the magnitude and inhomogeneous structures of the final profiles, and nicely describe the related eccentricity distributions $P({\epsilon }_n)$ (n=2, 3, 4). These results indicate that deep learning technique can capture the main features of the non-linear evolution of hydrodynamics, showing its potential to largely accelerate the event-by-event simulations of relativistic hydrodynamics.

7 pages, 4 figures

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• Machine learning approach to analyze heavy quark diffusion coefficient in relativistic heavy-ion collisions
• Non-equilibrium cumulants within model A from crossover to first-order phase transition side
• Ultra fast, event-by-event heavy-ion simulations for next generation experiments
• Fast prediction of the hydrodynamic QGP evolution in ultra-relativistic heavy-ion collisions using Fourier Neural Operators
• Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion
• Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions