A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913137081450496 |
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| author | Wu, Kai-Yi Yang, Zhong Pang, Long-Gang Wang, Xin-Nian |
| author_facet | Wu, Kai-Yi Yang, Zhong Pang, Long-Gang Wang, Xin-Nian |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_17511 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions Wu, Kai-Yi Yang, Zhong Pang, Long-Gang Wang, Xin-Nian Nuclear Theory High Energy Physics - Phenomenology 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. |
| title | A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions |
| topic | Nuclear Theory High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2605.17511 |