论文标题
CBM实验中重型离子碰撞的快速中心性表
A fast centrality-meter for heavy-ion collisions at the CBM experiment
论文作者
论文摘要
提出了一种基于深度学习的事件表征的新方法。 PointNet模型可用于CBM实验中的快速，在线事件的影响参数。在这项研究中，使用口气和CBM检测器仿真来在10 ageV处生成AU+AU碰撞事件，然后将其用于训练和评估基于PointNet的架构。可以对模型进行训练，例如诸如CBM检测器平面中颗粒的命中位置，从命中或其组合进行重建的轨迹。深度学习模型将影响参数从2-14 FM重建，平均误差从-0.33到0.22 FM不等。对于5-14 FM范围内的冲击参数，使用命中和跟踪粒子信息组合的模型的相对精度为4-9％，平均误差为-0.33至0.13 FM。在相同的影响参数范围内，只有轨道信息的模型的相对精度为4-10％，平均误差为-0.18至0.22 FM。这种新的事件分类方法比传统方法更准确，依赖性模型更少，并且可以利用现代GPU处理器单元的性能提升。
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9 % and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10 % and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.
