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arXiv 2302.09478nucl-thhep-phnucl-ex
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基于色玻璃凝聚初始条件的强相互作用物质贝叶斯量化

Bayesian quantification of strongly-interacting matter with color glass condensate initial conditions

Matthew R. Heffernan, Charles Gale, Sangyong Jeon, Jean-François Paquet

AI总结:

本研究结合IP-Glasma初始态等多阶段模型,对2.76 TeV铅+铅碰撞开展全局贝叶斯分析,获取强相互作用物质粘度等参数,首次在重离子分析中结合迁移学习推断与贝叶斯模型平均方法。

摘要:

我们对质心系能量$\u200b\sqrt{s}_{\rm NN}$=2.76 TeV的相对论性铅+铅碰撞开展了全局贝叶斯分析,采用的多阶段模型由IP-Glasma初始态、粘滞流体动力学演化和强子输运末态构成。研究考虑的可观测量来自软区强子末态。我们得到了与IP-Glasma阶段和流体动力学阶段相关的后验分布以及最大后验参数分布,包括强相互作用物质的切变和体比粘度。本工作首次在重离子分析中结合迁移学习进行推断,同时采用了贝叶斯模型平均(Bayes Model Averaging)方法。

英文摘要:

A global Bayesian analysis of relativistic Pb + Pb collisions at $\sqrt{s}_{\rm NN}$ = 2.76 TeV is performed, using a multistage model consisting of an IP-Glasma initial state, a viscous fluid dynamical evolution, and a hadronic transport final state. The observables considered are from the soft sector hadronic final state. Posterior and Maximum a Posteriori parameter distributions that pertain to the IP-Glasma and hydrodynamic phases are obtained, including the shear and bulk specific viscosity of strong interacting matter. The first use of inference with transfer learning in heavy-ion analyses is presented, together with Bayes Model Averaging.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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