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arXiv 2109.07191hep-ph
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Landau和人工神经网络(ANN)模型中基于快度的粒子多重性

Particles Multiplicity Based on Rapidity in Landau and Artificial Neural Network(ANN) Models

D. M. Habashy, Mahmoud Y. El-Bakry, Abdel Nasser Tawfik, R. M. Abdel Rahman, Mahmoud Hanafy

摘要:

利用人工神经网络模型估算了在能量范围为2-200 GeV的Au+Au中心碰撞实验中观测到的带电π介子和K介子的单位快度多重数,并与RHIC-BRAHMS、NICA和FAIR等现有实验数据进行了比较.我们还采用了Landau流体动力学方法,该方法能更好地描述超相对论重离子碰撞中产生的热密物质的演化。该方法与实验估计和神经网络模拟的结果都相符。我们注意到,朗道模型精确地再现了所有能量下所有产生的粒子每快度的多重性的整个范围。人工神经网络模型也可以很好地再现每快度的多重数。

英文摘要:

ANN model is used to estimate the multiplicity per rapidity for charged pions and kaons observed in various high-energy experiments from central Au+Au collisions with energies ranging from 2-200 GeV, and then compared to available experimental data, including RHIC-BRAHMS, and the future facilities at NICA and FAIR. We also used Landau hydrodynamical approach, which has a better describtion for the evolution of hot and dense matter produced in ultra-relativistic heavy-ion collisions. The approach is fitted to both results estimated from experiment and ANN simulation. We noticed that the Landau model accurately reproduces the entire range of multiplicity per rapidity for all created particles at all energies. Also ANN model can reproduce the multiplicity per rapidity very well for all considered particles.

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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