Open Access
| Issue |
EPJ Web Conf.
Volume 364, 2026
XXXI International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions “Quark Matter 2025”
|
|
|---|---|---|
| Article Number | 12001 | |
| Number of page(s) | 4 | |
| Section | New Theoretical Developments | |
| DOI | https://doi.org/10.1051/epjconf/202636412001 | |
| Published online | 17 April 2026 | |
- E. Shuryak, Strongly coupled quark-gluon plasma in heavy ion collisions, Rev. Mod. Phys. 89, 035001 (2017), 1412.8393. doi: 10.1103/RevModPhys.89.035001 [Google Scholar]
- U. Heinz, R. Snellings, Collective flow and viscosity in relativistic heavy-ion collisions, Ann. Rev. Nucl. Part. Sci. 63, 123 (2013), 1301.2826. doi: 10.1146/annurev-nucl-102212-170540 [Google Scholar]
- L. Yan, A flow paradigm in heavy-ion collisions, Chin. Phys. C 42, 042001 (2018), 1712.04580. doi: 10.1088/1674-1137/42/4/042001 [Google Scholar]
- C. Shen, L. Yan, Recent development of hydrodynamic modeling in heavy-ion collisions, Nucl. Sci. Tech. 31, 122 (2020), 2010.12377. doi: 10.1007/s41365-020-00829-z [Google Scholar]
- B. Schenke, S. Jeon, C. Gale, Elliptic and triangular flow in event-by-event (3+1)D viscous hydrodynamics, Phys. Rev. Lett. 106, 042301 (2011), 1009.3244. doi: 10.1103/Phys-RevLett.106.042301 [Google Scholar]
- B. Schenke, S. Jeon, C. Gale, (3+1)D hydrodynamic simulation of relativistic heavy-ion collisions, Phys. Rev. C 82, 014903 (2010), 1004.1408. doi: 10.1103/PhysRevC.82.014903 [Google Scholar]
- J.F. Paquet, C. Shen, G.S. Denicol, M. Luzum, B. Schenke, S. Jeon, C. Gale, Production of photons in relativistic heavy-ion collisions, Phys. Rev. C 93, 044906 (2016), 1509.06738. doi: 10.1103/PhysRevC.93.044906 [Google Scholar]
- C. Gale, J.F. Paquet, B. Schenke, C. Shen, Multimessenger heavy-ion collision physics, Phys. Rev. C 105, 014909 (2022), 2106.11216. doi: 10.1103/PhysRevC.105.014909 [Google Scholar]
- J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, in Proceedings of the 34th International Conference on Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY, USA, 2020), NIPS ’20, ISBN 9781713829546, https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf [Google Scholar]
- J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, S. Ganguli, Deep unsupervised learning using nonequilibrium thermodynamics, in International conference on machine learning (PMLR, 2015), pp. 2256-2265, https://proceedings.mlr.press/v37/sohl-dickstein15.pdf [Google Scholar]
- Y. Song, J. Sohl-Dickstein, D.P. Kingma, A. Kumar, S. Ermon, B. Poole, Score-Based Generative Modeling through Stochastic Differential Equations, in International Conference on Learning Representations (2021), https://openreview.net/forum?id= PxTIG12RRHS [Google Scholar]
- J.A. Sun, L. Yan, C. Gale, S. Jeon, An end-to-end generative diffusion model for heavyion collisions (2024), 2410.13069, https://arxiv.org/abs/2410.13069 [Google Scholar]
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