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2023
preprint
3D Multi-system Bayesian Calibration with Energy Conservation to Study Rapidity-dependent Dynamics of Nuclear Collisions
Andi Mankolli, A. Angerami, Ritu Arora, Steffen A. Bass, Shanshan Cao, Yi Chen, Lipei Du, R. J. Ehlers, Hannah Elfner, Wenkai Fan, Rainer J. Fries, Charles Gale, Yayun He, Ulrich Heinz, B. Jacak, Peter Martin Jacobs, Sangyong Jeon, Yi Ji, L. Kasper, M. Kordell, Amit Kumar, R. Kunnawalkam Elayavalli, Joseph Latessa, Sook H. Lee, Yen-Jie Lee, D. Liyanage, Matt Luzum, Abhijit Majumder, Simon Mak, Christal Martin, Haydar Mehryar, T. Mengel, James Declan Mulligan, C. Nattrass, Jean-François Paquet, Cameron Parker, Joern H. Putschke, G. Roland, Bjoern Schenke, Loren Schwiebert, Arjun Sengupta, Chun Shen, C. Sirimanna, R. A. Soltz, Ismail Soudi, Michael Strickland, Y. Tachibana, Julia Velkovska, G. Vujanovic, Xin-Nian Wang, W. Zhao
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Le résumé fourni par la source
Considerable information about the early-stage dynamics of heavy-ion collisions is encoded in the rapidity dependence of measurements. To leverage the large amount of experimental data, we perform a systematic analysis using three-dimensional hydrodynamic simulations of multiple collision systems -- large and small, symmetric and asymmetric. Specifically, we perform fully 3D multi-stage hydrodynamic simulations initialized by a parameterized model for rapidity-dependent energy deposition, which we calibrate on the hadron multiplicity and anisotropic flow coefficients. We utilize Bayesian inference to constrain properties of the early- and late- time dynamics of the system, and highlight the impact of enforcing global energy conservation in our 3D model.
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Les sujets associés
High-Energy Particle Collisions Researchdemographic modeling and climate adaptationMarkov Chains and Monte Carlo Methods