Speaker
Description
In high-energy heavy-ion collisions, jets traverse the quark-gluon plasma (QGP) and deposit energy into the medium, leading to jet-induced medium response. The medium response takes the form of Mach-cone-like excitations and can modify the internal structure of the jet, affecting many observables, such as jet shape and jet fragmentation function and so on. However, simulating jet-induced medium response requires not only a complete model that can accurately describe the evolution of hard and soft partons concurrently but also substantial computational resources for full-scale simulations. In this study, we trained a generative neural network using a flow model with gamma-jet events from 0-10$\%$ centrality Pb+Pb collisions at 5.02 TeV to estimate the final state effects of jet-induced medium response. Our findings indicate that with only the initial jet information—namely, the energy-momentum of gamma and the jet, along with their initial positions—the network can accurately predict the positions of the Mach-cone's leading edge and maintain a particle spectrum within the same order of magnitude as the actual data.
| Category | Theory |
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