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Applying Bayesian parameter estimation to relativistic heavy-ion collisions: Simultaneous characterization of the initial state and quark-gluon plasma medium

机译:将贝叶斯参数估计应用于相对论的重离子碰撞:初始状态和夸克 - 胶质等离子体介质的同时表征

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We quantitatively estimate properties of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions utilizing Bayesian statistics and a multiparameter model-to-data comparison. The study is performed using a recently developed parametric initial condition model, TRENTo, which interpolates among a general class of particle production schemes, and a modern hybrid model which couples viscous hydrodynamics to a hadronic cascade. We calibrate the model to multiplicity, transverse momentum, and flow data and report constraints on the parametrized initial conditions and the temperature-dependent transport coefficients of the quark-gluon plasma. We show that initial entropy deposition is consistent with a saturation-based picture, extract a relation between the minimum value and slope of the temperature-dependent specific shear viscosity, and find a clear signal for a nonzero bulk viscosity.
机译:我们利用贝叶斯统计和多游ameter模型对比较,我们定量估计在超弧度重离子碰撞中产生的夸克 - 胶质等离子体的特性。 该研究是使用最近开发的参数初始条件模型来执行的,该参数初始条件模型Trento在一般的粒子制作方案中插值,以及将粘性流体动力学的现代混合模型延伸到辐射级联。 我们将模型校准到多重性,横向动量和流量数据以及对参数化初始条件的报告约束以及夸克 - 胶质等离子体的温度相关的传输系数。 我们表明初始熵沉积与基于饱和的图像一致,提取温度依赖性特定剪切粘度的最小值和斜率之间的关系,并找到非零堆积粘度的清晰信号。

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