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机器学习方法分析相对论重离子碰撞中的重夸克扩散系数

Rui Guo¹, Yonghui Li², Baoyi Chen²

¹Data Science, Washington University, St. Louis, MO 63105, USA.

Entropy (Basel, Switzerland)

|November 24, 2023

概括

这项研究使用深 convolutional 神经网络 (CNN) 确定重夸克扩散系数在deconfined媒体. 该方法分析来自重离子碰撞的数据,以描述温度和动量依赖性.

科学领域:

核物理核物理核物理
高能物理高能物理
量子色态动力学量子色态动力学

背景情况:

了解重夸克在无约束介质中的行为对于研究夸克-子等离子体的特性至关重要.
来自相对论重离子碰撞的实验数据为重夸克动态提供了洞察力.
现有的模型需要精确确定重夸克扩散系数.

研究的目的:

为了确定重夸克在无限制介质中的扩散系数.
为了描述这种扩散系数的温度和动量依赖性.
评估实验数据不确定性对衍生的扩散系数的影响.

主要方法:

一个深层卷积神经网络 (CNN) 使用来自相对论重离子碰撞的数据进行训练.
使用了诸如核修饰因子 (RAA) 和来自B子衰变的非即时J/ψ的圆流 (v2) 等可观测因素.
用兰杰文方程和瞬间凝聚模型计算了B质子进化.

主要成果:

CNN成功输出了描述重夸克扩散系数温度和动量依赖性的参数.
来自各种碰撞中心的非提示J/ψ (RAA,v2) 的实验数据被用来推导出这些参数.
根据实验数据的不确定性,评估了扩散系数确定性的不确定性.

结论:

开发的CNN提供了一种强大的方法,可以从实验数据中提取重夸克扩散系数.
这项研究量化了重夸克扩散在无界介质中的温度和动量依赖性.
该方法考虑并评估来自实验输入的不确定性.

关键词:

扩散系数扩散系数重夸克是一个重夸克.机器学习是机器学习.夸克 - 子等离子体相对论重离子碰撞相对论重离子碰撞

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