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arXiv 2004.14409nucl-thcs.LGhep-phnucl-ex
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基于深度学习的相对论性重离子碰撞物态方程分类

Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning

University of Oslo(奥斯陆大学);Taras Shevchenko National University of Kyiv(基辅塔拉斯·舍甫琴科国立大学);Skobeltsyn Institute of Nuclear Physics, Moscow State University(莫斯科国立大学斯科别利岑核物理研究所);DataRoot Labs(DataRoot实验室)

Yu. Kvasiuk, E. Zabrodin, L. Bravina, I. Didur, M. Frolov

摘要:

卷积神经网络是一种强大的深度学习方法,被应用于UrQMD模型中产生的重离子碰撞事件的状态方程分类。质子的逐事件横向动量和方位角分布用于训练分类器。对$\sqrt{s_{NN}} = 11$ GeV的Au+Au事件的分类总精度达到98%.分类器的性能,在不同的碰撞能量的事件上训练,进行了研究。所获得的结果表明,深度学习方法在重离子碰撞物理学中的其他问题中具有广泛的应用可能性。

英文摘要:

Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle distributions of protons are used to train a classifier. An overall accuracy of classification of 98\% is reached for Au+Au events at $\sqrt{s_{NN}} = 11$ GeV. Performance of classifiers, trained on events at different colliding energies, is investigated. Obtained results indicate extensive possibilities of application of Deep Learning methods to other problems in physics of heavy-ion collisions.

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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