Coupling hadronic kinetic theory models to fluid dynamics in phenomenological studies of heavy ion collisions requires a prescription for "particlization". Existing particlization models are based on implicit or explicit assumptions about the microscopic degrees of freedom that go beyond the information provided by the preceding fluid dynamical history. We propose an alternative prescription which uses only macroscopic information provided by the hydrodynamic output. This method follows directly from the connections between information theory and statistical mechanics.
机构:
Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
Henan Normal Univ, Coll Phys & Mat Sci, Xinxiang 453007, Peoples R ChinaChinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
Chen, Jinhui
Keane, Declan
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Kent State Univ, Kent, OH 44242 USAChinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
Keane, Declan
Ma, Yu-Gang
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Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
Univ Chinese Acad Sci, Beijing 100049, Peoples R ChinaChinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
Ma, Yu-Gang
Tang, Aihong
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Brookhaven Natl Lab, Upton, NY 11973 USAChinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
Tang, Aihong
Xu, Zhangbu
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Brookhaven Natl Lab, Upton, NY 11973 USA
Shandong Univ, Jinan 250100, Shandong, Peoples R ChinaChinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China
机构:
Massachusetts Institute of Technology, Cambridge,02139-4307, United StatesMassachusetts Institute of Technology, Cambridge,02139-4307, United States
Roland, G.
1600,
Springer Science and Business Media, LLC
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