Classification of processes in heavy-ion collisions in the CBM experiment (FAIR/GSI, Darmstadt) using neural networks is investigated. Fully-connected neural networks and a deep convolutional neural network are built to identify quark–gluon plasma simulated within the Parton-Hadron-String Dynamics (PHSD) microscopic off-shell transport approach for central Au+Au collision at a fixed energy. The convolutional neural network outperforms fully-connected networks and reaches 93% accuracy on the validation set, while the remaining only 7% of collisions are incorrectly classified.

中文翻译：

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CBM 实验中夸克-胶子等离子体检测的深度学习

研究了使用神经网络对 CBM 实验 (FAIR/GSI, Darmstadt) 中重离子碰撞过程的分类。建立完全连接的神经网络和深度卷积神经网络，以识别在固定能量下的中心 Au+Au 碰撞的 Parton-Hadron-String Dynamics (PHSD) 微观壳外传输方法中模拟的夸克-胶子等离子体。卷积神经网络优于全连接网络，在验证集上达到 93% 的准确率，而剩下的只有 7% 的碰撞被错误分类。