论文标题
使用深度学习中的相对论重型离子碰撞中国家方程式分类
Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
论文作者
论文摘要
卷积神经网是一种强大的深度学习方法，用于对URQMD模型中产生的重离子碰撞事件状态的方程进行分类。 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.
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.
