A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions

Abstract: In high-energy heavy-ion collisions, propagation of the energy deposited into the medium by energetic partons that traverse the quark-gluon plasma (QGP) leads to Mach-cone-like jet-induced medium response. Full simulations of such jet-induced medium responses require a complete model such as the coupled Linear Boltzmann Transport and hydrodynamic (CoLBT-hydro) model that can carry out the concurrent evolution of both hard partons and the medium. Such full simulations on parallelized computers, however, are very resource-intensive and alternative simulation methods will be useful for more extensive physics investigations. In this study, we train a Flow Matching generative model with $γ$-jet events in 0-10$\%$ Pb+Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02 TeV from the CoLBT-hydro model to estimate the final-state hadron spectra $d^3N/dp_Tdηdφ$ from jet-induced hydro response. With only the initial spatial and momentum information of the $γ$ and jets, the network is shown to conditionally generate the marginal final-state hadron spectra from the jet-induced hydro response that agree well with the training data. This generative model achieves a computational acceleration of approximately six orders of magnitude compared to the full CoLBT-hydro simulations, while faithfully preserving the statistical properties of the front and diffusion wake of the Mach-cone-like hydro response and their contributions to the hadron spectra.